System

Abstract

An object of a system according to an embodiment is to provide a specific action plan based on a situation and a goal of a user.SOLUTION: A system according to an embodiment includes a situation grasping unit, an application proposing unit, and an action plan providing unit. The situation grasping unit grasps a situation and a goal of the user. The application proposal unit proposes a teaching application method on the basis of the situation of the user grasped by the situation grasping unit and the goal. The action plan providing section provides an action plan based on the teaching application method proposed by the application proposing 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] The current technology leaves room for improvement as it does not provide concrete examples of how to apply self-improvement teachings to real-life situations.

[0005] The system according to the embodiment aims to provide a specific action plan based on the user's situation and goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a situation grasping unit, an application suggestion unit, and an action plan providing unit. The situation grasping unit grasps the situation and goals of a user. The application suggestion unit proposes a method of applying a teaching based on the situation and goals of the user grasped by the situation grasping unit. The action plan providing unit provides an action plan based on the method of applying the teaching proposed by the application suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a specific action plan based on the user's situation and goals. [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 self-development support system according to the embodiment of the present invention is a system that provides a specific action plan based on the user's situation and goals, thereby enabling the user to apply the teachings of self-development to their real life and obtain specific guidelines for action.

[0029] A self-development support system according to an embodiment includes a situation assessment unit, an application suggestion unit, and an action plan providing unit. The situation assessment unit assesses a user's situation and goals. For example, it collects information such as the user's living environment, occupation, and health condition. The situation assessment unit can also assess the user's short-term, long-term, personal, and professional goals. For example, the user inputs the goals they want to achieve and their current situation, and the situation assessment unit analyzes this information. The application suggestion unit suggests a method for applying teachings based on the user's situation and goals assessed by the situation assessment unit. For example, it makes specific suggestions about how the user should act in their daily lives based on the self-development teachings they have learned. The application suggestion unit can also suggest an optimal method for applying the teachings based on the user's learning style and lifestyle. For example, it provides learning materials using diagrams and graphs to visual learners and audio learning materials to auditory learners. The action plan providing unit provides an action plan based on the application method of the teachings suggested by the application suggestion unit. For example, it provides specific steps for the user to put into practice and monitors their progress. The action plan providing unit can also collect user feedback and modify the action plan as needed. For example, the system evaluates the effectiveness of the action plan implemented by the user and reflects this in the next action plan. This allows the self-development support system according to the embodiment to enable the user to apply the teachings of self-development to their actual lives and obtain specific guidelines for action. For example, after reading "The Seven Habits," the user can specifically understand and practice how to act in their daily lives. Furthermore, by following the action plan provided by the generation AI, the user can take specific steps toward achieving their goals.

[0030] The situation understanding unit can analyze a user's past behavioral history or social media posts to extract the user's latent goals or interests. For example, the situation understanding unit analyzes the user's past behavioral history to identify the activities and interests of the user. For example, latent goals can be extracted based on events the user has attended in the past or the content of newsletters the user has subscribed to. The situation understanding unit can also analyze social media posts to identify topics and interests frequently mentioned by the user. For example, the content of posts and hashtags can be analyzed to identify the user's interests. The situation understanding unit can also analyze the user's online activity history to identify what websites and content the user has viewed. For example, latent goals and interests can be extracted based on browsing history and search history. This allows the user's latent goals and interests to be identified, thereby providing a more appropriate action plan.

[0031] The situation assessment unit can measure the user's psychological state or stress level using a biosensor and optimize goal setting based on the result. The situation assessment unit, for example, uses the biosensor to measure the user's heart rate and electrodermal activity and analyze the psychological state and stress level. For example, the stress level is evaluated based on heart rate variability. The situation assessment unit also uses a wearable device to collect data in real time to measure the user's psychological state. For example, a smartwatch is used to monitor the heart rate and sleep patterns. The situation assessment unit also evaluates the user's stress level based on the data from the biosensor and sets goals accordingly. For example, if stress is high, a goal encouraging relaxation is set. This allows goal setting to be optimized based on the user's psychological state and stress level.

[0032] The situation assessment unit can collect feedback from the user's family or friends and set goals from a third-party perspective. The situation assessment unit, for example, collects feedback from the user's family and friends and sets goals based on their opinions. For example, the feedback is obtained through questionnaires or interviews. The situation assessment unit also builds a platform for collecting the opinions of family and friends and reflects them in the user's goal setting. For example, it uses an online form or a chatbot. The situation assessment unit also optimizes the user's goal setting based on feedback from a third-party perspective. For example, it aggregates the opinions of family and friends and sets common goals. In this way, goal setting from a third-party perspective can provide a more objective action plan.

[0033] The situation understanding unit can compare the goal setting methods of users from different cultures or regions and propose optimal goals from a global perspective. The situation understanding unit, for example, collects and compares the goal setting methods of users from different cultures and regions. For example, it investigates self-development teachings and goal setting trends in each region. The situation understanding unit also integrates data from different cultures and regions to propose optimal goals from a global perspective. For example, it sets goals based on success stories from each region. The situation understanding unit also refers to the goal setting methods of users from different cultures and regions and proposes optimal goals to the user. For example, it sets goals taking cultural background and regional characteristics into consideration. This makes it possible to provide a more diverse range of action plans by proposing optimal goals from a global perspective.

[0034] The application suggestion unit can analyze the user's learning style and, based on that, suggest an optimal teaching application method. For example, the application suggestion unit collects visual, auditory, and tactile data to analyze the user's learning style. For example, the learning style is identified through a questionnaire or test. The application suggestion unit also suggests an optimal teaching application method based on the learning style. For example, it provides teaching materials using diagrams and graphs to visual learners and audio teaching materials to auditory learners. The application suggestion unit also analyzes the user's learning style and, based on that, suggests a customized teaching application method. For example, it provides audio teaching materials to auditory learners and provides practical workshops to experiential learners. In this way, it is possible to suggest an optimal teaching application method based on the user's learning style.

[0035] The application suggestion unit can analyze the user's life rhythm or daily routine and suggest an application method for applying the teachings at the most effective timing. The application suggestion unit, for example, collects data to analyze the user's life rhythm or daily routine. For example, it analyzes smartphone usage history and calendar data. The application suggestion unit also suggests a method for applying the teachings at the most effective timing based on the user's life rhythm. For example, it suggests that the user learn the teachings during a time when the user is relaxed. The application suggestion unit also analyzes the user's daily routine and customizes the application method of the teachings based on the analysis. For example, it suggests a method for learning the teachings using commuting time or break time. This allows the teachings to be applied at the optimal timing based on the user's life rhythm.

[0036] The application suggestion unit can combine teachings from different fields and suggest a combined application method that is most suitable for the user's situation. For example, the application suggestion unit collects teachings from different fields and suggests a combined application method that is most suitable for the user's situation. For example, combining teachings on business and health. The application suggestion unit also analyzes teachings from different fields and suggests an application method that is most suitable for the user's goals. For example, combining teachings on leadership and communication. The application suggestion unit also suggests ways to customize and apply teachings from different fields according to the user's situation. For example, combining teachings on time management and stress management. In this way, by combining teachings from different fields, a more effective application method can be suggested.

[0037] The application suggestion unit can analyze the user's past successful experiences and suggest a method for applying the teachings based on the success patterns. The application suggestion unit, for example, collects the user's past successful experiences and analyzes their patterns. For example, it identifies success patterns based on past projects and achieved goals. The application suggestion unit also suggests an optimal method for applying the teachings based on the success patterns. For example, it provides specific steps for reproducing the past successful experiences. The application suggestion unit also analyzes the user's success patterns and suggests a customized method for applying the teachings based on them. For example, it sets new goals based on successful methods. In this way, it is possible to suggest an optimal method for applying the teachings based on the user's past successful experiences.

[0038] The action plan providing unit can monitor the user's progress in detail and provide feedback in real time. For example, the action plan providing unit collects data in real time to monitor the user's progress. For example, it records progress data through a smartphone app. The action plan providing unit also builds a system that provides feedback in real time based on the progress. For example, it displays the degree of goal achievement and progress rate and suggests the next step. The action plan providing unit also analyzes the user's progress data and modifies the action plan as necessary. For example, if progress is lagging, it provides additional support or advice. This makes it possible to monitor the user's progress in real time and provide appropriate feedback.

[0039] The action plan providing unit can analyze the user's behavioral data and continuously optimize the most effective action plan. The action plan providing unit, for example, identifies the most effective action plan by collecting and analyzing the user's behavioral data. For example, it performs analysis based on daily activity logs and task completion status. The action plan providing unit also builds a system that continuously optimizes the action plan based on the behavioral data. For example, it adjusts the plan by reflecting the user's progress and feedback. The action plan providing unit also analyzes the user's behavioral data in real time and provides the optimal action plan. For example, it suggests effective steps based on behavioral patterns and habits. This makes it possible to optimize the action plan based on the user's behavioral data.

[0040] The action plan providing unit can introduce a mechanism for forming a community of users and providing mutual feedback to each other. The action plan providing unit, for example, builds a community platform for users to provide each other with feedback. For example, it uses an online forum or chat group. The action plan providing unit also collects feedback within the community and reflects it in the user's action plan. For example, it shares advice and success stories from other users. The action plan providing unit also introduces a mechanism for users to support each other and promote the implementation of the action plan. For example, it sets up pair work or group challenges. This allows users to form a community of users and provide each other with feedback, thereby promoting the implementation of the action plan.

[0041] The action plan providing unit can incorporate advice from experts in different fields and provide a comprehensive action plan. For example, the action plan providing unit collects advice from experts in different fields and reflects it in the user's action plan. For example, it incorporates the opinions of a business coach or health advisor. The action plan providing unit also builds a system that provides a comprehensive action plan based on the advice of experts. For example, it proposes a plan that combines knowledge from multiple fields. The action plan providing unit also works with experts in different fields to provide comprehensive support to users. For example, it holds regular expert seminars and workshops. This makes it possible to incorporate advice from experts in different fields and provide a more comprehensive action plan.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The situation understanding unit can analyze a user's past behavioral history or social media posts to extract the user's potential goals or interests. For example, potential goals can be extracted based on events the user has attended in the past or the content of newsletters the user subscribes to. It can also analyze social media posts to identify topics and interests frequently mentioned by the user. It can also analyze online activity history to identify the websites and content the user has viewed. This allows the system to understand the user's potential goals and interests and provide a more appropriate action plan.

[0044] The situation assessment unit can use a biosensor to measure the user's psychological state or stress level and optimize goal setting based on that. For example, it can measure heart rate and electrodermal activity to analyze the psychological state and stress level. It can also use a wearable device to collect data in real time and monitor heart rate and sleep patterns. Furthermore, by setting goals according to stress levels, it is possible to provide optimal goals based on the user's psychological state.

[0045] The situation assessment unit can collect feedback from the user's family or friends and set goals from a third-party perspective. For example, feedback can be obtained through questionnaires or interviews. Alternatively, a platform for collecting opinions from family and friends can be built, and online forms or chatbots can be used. Furthermore, common goals can be set based on feedback from a third-party perspective. This allows for goal setting from a third-party perspective, providing a more objective action plan.

[0046] The situation assessment unit can compare the goal-setting methods of users from different cultures or regions and propose optimal goals from a global perspective. For example, it can investigate self-development teachings and goal-setting trends in each region. It can also integrate data from different cultures and regions and set goals based on success stories from each region. Furthermore, by setting goals that take cultural background and regional characteristics into consideration, it is possible to propose optimal goals from a global perspective. This allows for a wider variety of action plans to be provided.

[0047] The application suggestion unit can analyze a user's learning style and suggest the most appropriate teaching application method based on that. For example, it can collect visual, auditory, and tactile data and identify learning styles through questionnaires and tests. It can also provide visual learners with teaching materials using diagrams and graphs, and auditory learners with audio materials. It can also provide hands-on workshops for experience-oriented learners, thereby suggesting the most appropriate teaching application method based on the user's learning style.

[0048] The application suggestion unit can analyze the user's lifestyle or daily routine and suggest an application method for applying the teachings at the most effective timing. For example, it can analyze the smartphone usage history or calendar data. It can also suggest learning the teachings during times when the user is relaxing based on the user's lifestyle. Furthermore, by suggesting ways to learn the teachings using commuting time or break time, it is possible to apply the teachings at the optimal timing based on the user's lifestyle.

[0049] The application suggestion unit can combine teachings from different fields to suggest a combined application method that is most suitable for the user's situation. For example, it can combine teachings on business and health, or on leadership and communication. Furthermore, it can combine teachings on time management and stress management, thereby suggesting a more effective application method by combining teachings from different fields.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The situation understanding unit understands the user's situation and goals. For example, it collects information about the user's living environment, occupation, health condition, etc. It can also understand the user's short-term goals, long-term goals, personal goals, and professional goals. The situation understanding unit analyzes this information by having the user input the goals they want to achieve and their current situation. Step 2: The application suggestion unit suggests how to apply the teachings based on the user's situation and goals ascertained by the situation understanding unit. For example, it makes specific suggestions on how the user should act in their daily life based on the self-development teachings they have learned. It can also suggest the optimal way to apply the teachings based on the user's learning style and daily rhythm. Visual learners are provided with teaching materials using diagrams and graphs, and auditory learners are provided with audio materials. Step 3: The action plan provider provides an action plan based on the application method of the teachings suggested by the application suggester. For example, it provides specific steps for the user to put into practice and monitors the progress. It can also collect user feedback and revise the action plan as needed. It evaluates how effective the action plan implemented by the user was and reflects this in the next action plan.

[0052] (Example 2) The self-development support system according to the embodiment of the present invention is a system that provides a specific action plan based on the user's situation and goals, thereby enabling the user to apply the teachings of self-development to their real life and obtain specific guidelines for action.

[0053] A self-development support system according to an embodiment includes a situation assessment unit, an application suggestion unit, and an action plan providing unit. The situation assessment unit assesses a user's situation and goals. For example, it collects information such as the user's living environment, occupation, and health condition. The situation assessment unit can also assess the user's short-term, long-term, personal, and professional goals. For example, the user inputs the goals they want to achieve and their current situation, and the situation assessment unit analyzes this information. The application suggestion unit suggests a method for applying teachings based on the user's situation and goals assessed by the situation assessment unit. For example, it makes specific suggestions about how the user should act in their daily lives based on the self-development teachings they have learned. The application suggestion unit can also suggest an optimal method for applying the teachings based on the user's learning style and lifestyle. For example, it provides learning materials using diagrams and graphs to visual learners and audio learning materials to auditory learners. The action plan providing unit provides an action plan based on the application method of the teachings suggested by the application suggestion unit. For example, it provides specific steps for the user to put into practice and monitors their progress. The action plan providing unit can also collect user feedback and modify the action plan as needed. For example, the system evaluates the effectiveness of the action plan implemented by the user and reflects this in the next action plan. This allows the self-development support system according to the embodiment to enable the user to apply the teachings of self-development to their actual lives and obtain specific guidelines for action. For example, after reading "The Seven Habits," the user can specifically understand and practice how to act in their daily lives. Furthermore, by following the action plan provided by the generation AI, the user can take specific steps toward achieving their goals.

[0054] The situation understanding unit can analyze a user's past behavioral history or social media posts to extract the user's latent goals or interests. For example, the situation understanding unit analyzes the user's past behavioral history to identify the activities and interests of the user. For example, latent goals can be extracted based on events the user has attended in the past or the content of newsletters the user has subscribed to. The situation understanding unit can also analyze social media posts to identify topics and interests frequently mentioned by the user. For example, the content of posts and hashtags can be analyzed to identify the user's interests. The situation understanding unit can also analyze the user's online activity history to identify what websites and content the user has viewed. For example, latent goals and interests can be extracted based on browsing history and search history. This allows the user's latent goals and interests to be identified, thereby providing a more appropriate action plan.

[0055] The situation assessment unit can measure the user's psychological state or stress level using a biosensor and optimize goal setting based on the result. The situation assessment unit, for example, uses the biosensor to measure the user's heart rate and electrodermal activity and analyze the psychological state and stress level. For example, the stress level is evaluated based on heart rate variability. The situation assessment unit also uses a wearable device to collect data in real time to measure the user's psychological state. For example, a smartwatch is used to monitor the heart rate and sleep patterns. The situation assessment unit also evaluates the user's stress level based on the data from the biosensor and sets goals accordingly. For example, if stress is high, a goal encouraging relaxation is set. This allows goal setting to be optimized based on the user's psychological state and stress level.

[0056] The situation understanding unit can use the emotion estimation function to analyze the emotions of the user when learning self-improvement teachings and set goals based on those emotions. The situation understanding unit, for example, uses the emotion estimation function to analyze the emotions of the user when learning self-improvement teachings in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The situation understanding unit also collects emotion data when the user learns self-improvement teachings and sets goals based on that data. For example, it sets goals based on teachings that evoke strong positive emotions. The situation understanding unit also uses the emotion estimation function to analyze the emotional reactions of the user to the teachings they have learned and suggests optimal goals based on those emotions. For example, it sets goals based on teachings that have a high emotion score. In this way, by setting goals based on the user's emotions, a more effective action plan can be provided.

[0057] The situation assessment unit can collect feedback from the user's family or friends and set goals from a third-party perspective. The situation assessment unit, for example, collects feedback from the user's family and friends and sets goals based on their opinions. For example, the feedback is obtained through questionnaires or interviews. The situation assessment unit also builds a platform for collecting the opinions of family and friends and reflects them in the user's goal setting. For example, it uses an online form or a chatbot. The situation assessment unit also optimizes the user's goal setting based on feedback from a third-party perspective. For example, it aggregates the opinions of family and friends and sets common goals. In this way, goal setting from a third-party perspective can provide a more objective action plan.

[0058] The situation understanding unit can compare the goal setting methods of users from different cultures or regions and propose optimal goals from a global perspective. The situation understanding unit, for example, collects and compares the goal setting methods of users from different cultures and regions. For example, it investigates self-development teachings and goal setting trends in each region. The situation understanding unit also integrates data from different cultures and regions to propose optimal goals from a global perspective. For example, it sets goals based on success stories from each region. The situation understanding unit also refers to the goal setting methods of users from different cultures and regions and proposes optimal goals to the user. For example, it sets goals taking cultural background and regional characteristics into consideration. This makes it possible to provide a more diverse range of action plans by proposing optimal goals from a global perspective.

[0059] The situation understanding unit can use the emotion estimation function to monitor the emotions of the user when setting goals in real time and suggest goals that elicit positive emotions. The situation understanding unit, for example, uses the emotion estimation function to monitor the emotions of the user when setting goals in real time. For example, it analyzes facial expressions and voice tone to calculate an emotion score. The situation understanding unit also suggests goals that elicit positive emotions based on the user's emotion data. For example, it prioritizes setting goals with high emotion scores. The situation understanding unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when the user sets goals. For example, it presents encouraging messages and success stories. This makes it possible to provide a more effective action plan by setting goals based on the user's emotions.

[0060] The application suggestion unit can analyze the user's learning style and, based on that, suggest an optimal teaching application method. For example, the application suggestion unit collects visual, auditory, and tactile data to analyze the user's learning style. For example, the learning style is identified through a questionnaire or test. The application suggestion unit also suggests an optimal teaching application method based on the learning style. For example, it provides teaching materials using diagrams and graphs to visual learners and audio teaching materials to auditory learners. The application suggestion unit also analyzes the user's learning style and, based on that, suggests a customized teaching application method. For example, it provides audio teaching materials to auditory learners and provides practical workshops to experiential learners. In this way, it is possible to suggest an optimal teaching application method based on the user's learning style.

[0061] The application suggestion unit can analyze the user's life rhythm or daily routine and suggest an application method for applying the teachings at the most effective timing. The application suggestion unit, for example, collects data to analyze the user's life rhythm or daily routine. For example, it analyzes smartphone usage history and calendar data. The application suggestion unit also suggests a method for applying the teachings at the most effective timing based on the user's life rhythm. For example, it suggests that the user learn the teachings during a time when the user is relaxed. The application suggestion unit also analyzes the user's daily routine and customizes the application method of the teachings based on the analysis. For example, it suggests a method for learning the teachings using commuting time or break time. This allows the teachings to be applied at the optimal timing based on the user's life rhythm.

[0062] The application suggestion unit can use the emotion estimation function to analyze the emotions the user felt when learning a teaching, and suggest an application method based on those emotions. The application suggestion unit, for example, uses the emotion estimation function to analyze the emotions the user felt when learning a teaching in real time. For example, it analyzes facial expressions and vocal tone and calculates an emotion score. The application suggestion unit also suggests an optimal application method for a teaching based on the user's emotion data. For example, it prioritizes the application of teachings that evoke strong positive emotions. The application suggestion unit also uses the emotion estimation function to analyze the user's emotional response when learning a teaching, and customizes the application method based on those emotions. For example, it suggests an application method based on teachings that have a high emotion score. This makes it possible to suggest an optimal application method for a teaching based on the user's emotions.

[0063] The application suggestion unit can combine teachings from different fields and suggest a combined application method that is most suitable for the user's situation. For example, the application suggestion unit collects teachings from different fields and suggests a combined application method that is most suitable for the user's situation. For example, combining teachings on business and health. The application suggestion unit also analyzes teachings from different fields and suggests an application method that is most suitable for the user's goals. For example, combining teachings on leadership and communication. The application suggestion unit also suggests ways to customize and apply teachings from different fields according to the user's situation. For example, combining teachings on time management and stress management. In this way, by combining teachings from different fields, a more effective application method can be suggested.

[0064] The application suggestion unit can analyze the user's past successful experiences and suggest a method for applying the teachings based on the success patterns. The application suggestion unit, for example, collects the user's past successful experiences and analyzes their patterns. For example, it identifies success patterns based on past projects and achieved goals. The application suggestion unit also suggests an optimal method for applying the teachings based on the success patterns. For example, it provides specific steps for reproducing the past successful experiences. The application suggestion unit also analyzes the user's success patterns and suggests a customized method for applying the teachings based on them. For example, it sets new goals based on successful methods. In this way, it is possible to suggest an optimal method for applying the teachings based on the user's past successful experiences.

[0065] The application suggestion unit can use the emotion estimation function to monitor the emotions of the user when applying the teachings in real time and suggest an application method that elicits positive emotions. The application suggestion unit, for example, uses the emotion estimation function to monitor the emotions of the user when applying the teachings in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The application suggestion unit also suggests an application method that elicits positive emotions based on the user's emotion data. For example, it prioritizes application methods with high emotion scores. The application suggestion unit also uses the emotion estimation function to provide an interface that elicits positive emotions when the user applies the teachings. For example, it presents encouraging messages and success stories. This makes it possible to suggest the optimal application method of the teachings based on the user's emotions.

[0066] The action plan providing unit can monitor the user's progress in detail and provide feedback in real time. For example, the action plan providing unit collects data in real time to monitor the user's progress. For example, it records progress data through a smartphone app. The action plan providing unit also builds a system that provides feedback in real time based on the progress. For example, it displays the degree of goal achievement and progress rate and suggests the next step. The action plan providing unit also analyzes the user's progress data and modifies the action plan as necessary. For example, if progress is lagging, it provides additional support or advice. This makes it possible to monitor the user's progress in real time and provide appropriate feedback.

[0067] The action plan providing unit can analyze the user's behavioral data and continuously optimize the most effective action plan. The action plan providing unit, for example, identifies the most effective action plan by collecting and analyzing the user's behavioral data. For example, it performs analysis based on daily activity logs and task completion status. The action plan providing unit also builds a system that continuously optimizes the action plan based on the behavioral data. For example, it adjusts the plan by reflecting the user's progress and feedback. The action plan providing unit also analyzes the user's behavioral data in real time and provides the optimal action plan. For example, it suggests effective steps based on behavioral patterns and habits. This makes it possible to optimize the action plan based on the user's behavioral data.

[0068] The action plan providing unit can use the emotion estimation function to analyze the emotions of the user when putting the action plan into practice and adjust the plan based on those emotions. The action plan providing unit, for example, uses the emotion estimation function to analyze the emotions of the user when putting the action plan into practice in real time. For example, it analyzes facial expressions and vocal tone and calculates an emotion score. The action plan providing unit also adjusts the action plan based on the user's emotion data. For example, it prioritizes steps that evoke strong positive emotions. The action plan providing unit also uses the emotion estimation function to analyze the user's emotional reactions when putting the action plan into practice and customizes the plan based on those emotions. For example, it adjusts the plan based on steps that have a high emotion score. This makes it possible to adjust the action plan based on the user's emotions.

[0069] The action plan providing unit can introduce a mechanism for forming a community of users and providing mutual feedback to each other. The action plan providing unit, for example, builds a community platform for users to provide each other with feedback. For example, it uses an online forum or chat group. The action plan providing unit also collects feedback within the community and reflects it in the user's action plan. For example, it shares advice and success stories from other users. The action plan providing unit also introduces a mechanism for users to support each other and promote the implementation of the action plan. For example, it sets up pair work or group challenges. This allows users to form a community of users and provide each other with feedback, thereby promoting the implementation of the action plan.

[0070] The action plan providing unit can incorporate advice from experts in different fields and provide a comprehensive action plan. For example, the action plan providing unit collects advice from experts in different fields and reflects it in the user's action plan. For example, it incorporates the opinions of a business coach or health advisor. The action plan providing unit also builds a system that provides a comprehensive action plan based on the advice of experts. For example, it proposes a plan that combines knowledge from multiple fields. The action plan providing unit also works with experts in different fields to provide comprehensive support to users. For example, it holds regular expert seminars and workshops. This makes it possible to incorporate advice from experts in different fields and provide a more comprehensive action plan.

[0071] The action plan providing unit can use the emotion estimation function to monitor the emotions of the user in real time when putting the action plan into practice, and provide support for drawing out positive emotions. The action plan providing unit, for example, uses the emotion estimation function to monitor the emotions of the user when putting the action plan into practice in real time. For example, it analyzes facial expressions and vocal tone to calculate an emotion score. The action plan providing unit also provides support for drawing out positive emotions based on the user's emotion data. For example, it prioritizes steps with high emotion scores. The action plan providing unit also uses the emotion estimation function to provide an interface for drawing out positive emotions when the user puts the action plan into practice. For example, it presents encouraging messages and success stories. This allows support for putting the action plan into practice based on the user's emotions, enabling more effective practice.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The situation understanding unit can analyze a user's past behavioral history or social media posts to extract the user's potential goals or interests. For example, potential goals can be extracted based on events the user has attended in the past or the content of newsletters the user subscribes to. It can also analyze social media posts to identify topics and interests frequently mentioned by the user. It can also analyze online activity history to identify the websites and content the user has viewed. This allows the system to understand the user's potential goals and interests and provide a more appropriate action plan.

[0074] The situation assessment unit can use a biosensor to measure the user's psychological state or stress level and optimize goal setting based on that. For example, it can measure heart rate and electrodermal activity to analyze the psychological state and stress level. It can also use a wearable device to collect data in real time and monitor heart rate and sleep patterns. Furthermore, by setting goals according to stress levels, it is possible to provide optimal goals based on the user's psychological state.

[0075] The situation understanding unit can use the emotion estimation function to analyze the emotions the user felt when learning self-improvement teachings and set goals based on those emotions. For example, it can analyze facial expressions and voice tone to calculate an emotion score. It can also collect emotion data and set goals based on teachings that elicit strong positive emotions. It can also analyze emotional responses and suggest optimal goals based on teachings with high emotion scores. This allows for a more effective action plan to be provided by setting goals based on the user's emotions.

[0076] The situation assessment unit can collect feedback from the user's family or friends and set goals from a third-party perspective. For example, feedback can be obtained through questionnaires or interviews. Alternatively, a platform for collecting opinions from family and friends can be built, and online forms or chatbots can be used. Furthermore, common goals can be set based on feedback from a third-party perspective. This allows for goal setting from a third-party perspective, providing a more objective action plan.

[0077] The situation assessment unit can compare the goal-setting methods of users from different cultures or regions and propose optimal goals from a global perspective. For example, it can investigate self-development teachings and goal-setting trends in each region. It can also integrate data from different cultures and regions and set goals based on success stories from each region. Furthermore, by setting goals that take cultural background and regional characteristics into consideration, it is possible to propose optimal goals from a global perspective. This allows for a wider variety of action plans to be provided.

[0078] The situation understanding unit uses the emotion estimation function to monitor the user's emotions in real time when setting goals, and can suggest goals that elicit positive emotions. For example, it can analyze facial expressions and voice tone to calculate an emotion score. It can also prioritize setting goals with high emotion scores based on the emotion data. Furthermore, it can use the emotion estimation function to suggest goals that elicit positive emotions by presenting encouraging messages and success stories. This makes it possible to provide a more effective action plan by setting goals based on the user's emotions.

[0079] The application suggestion unit can analyze a user's learning style and suggest the most appropriate teaching application method based on that. For example, it can collect visual, auditory, and tactile data and identify learning styles through questionnaires and tests. It can also provide visual learners with teaching materials using diagrams and graphs, and auditory learners with audio materials. It can also provide hands-on workshops for experience-oriented learners, thereby suggesting the most appropriate teaching application method based on the user's learning style.

[0080] The application suggestion unit can analyze the user's lifestyle or daily routine and suggest an application method for applying the teachings at the most effective timing. For example, it can analyze the smartphone usage history or calendar data. It can also suggest learning the teachings during times when the user is relaxing based on the user's lifestyle. Furthermore, by suggesting ways to learn the teachings using commuting time or break time, it is possible to apply the teachings at the optimal timing based on the user's lifestyle.

[0081] The application suggestion unit can use the emotion estimation function to analyze the emotions the user felt when learning a teaching and suggest an application method based on those emotions. For example, it can analyze facial expressions and voice tones to calculate an emotion score. It can also prioritize the application of teachings that evoke strong positive emotions based on the emotion data. Furthermore, by analyzing the emotional response and suggesting an application method based on teachings with high emotion scores, it is possible to suggest the optimal application method for a teaching based on the user's emotions.

[0082] The application suggestion unit can combine teachings from different fields to suggest a combined application method that is most suitable for the user's situation. For example, it can combine teachings on business and health, or on leadership and communication. Furthermore, it can combine teachings on time management and stress management, thereby suggesting a more effective application method by combining teachings from different fields.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The situation understanding unit understands the user's situation and goals. For example, it collects information about the user's living environment, occupation, health condition, etc. It can also understand the user's short-term goals, long-term goals, personal goals, and professional goals. The situation understanding unit analyzes this information by having the user input the goals they want to achieve and their current situation. Step 2: The application suggestion unit suggests how to apply the teachings based on the user's situation and goals ascertained by the situation understanding unit. For example, it makes specific suggestions on how the user should act in their daily life based on the self-development teachings they have learned. It can also suggest the optimal way to apply the teachings based on the user's learning style and daily rhythm. Visual learners are provided with teaching materials using diagrams and graphs, and auditory learners are provided with audio materials. Step 3: The action plan provider provides an action plan based on the application method of the teachings suggested by the application suggester. For example, it provides specific steps for the user to put into practice and monitors the progress. It can also collect user feedback and revise the action plan as needed. It evaluates how effective the action plan implemented by the user was and reflects this in the next action plan.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 situation grasping unit that grasps a situation and a goal of a user; an application suggestion unit that suggests a method for applying a teaching based on the situation and goal of the user grasped by the situation grasping unit; an action plan providing unit that provides an action plan based on the application method of the teaching suggested by the application suggestion unit. A system characterized by:

2. The situation grasping unit Using biosensors to measure the user's psychological state or stress level and optimize goal setting accordingly 2. The system of claim 1.

3. The situation grasping unit Compare goal setting methods of users from different cultures or regions and propose optimal goals from a global perspective 2. The system of claim 1.

4. The application suggestion unit Analyzing the emotions felt by the user when learning the teachings and suggesting the application method based on the emotions.

2. The system of claim 1.

5. The action plan providing unit Analyzing emotions when the user puts the action plan into practice and adjusting the plan based on the emotions 2. The system of claim 1.

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