System

Abstract

An object of the system according to the embodiment is to track a user's task management and progress to form a positive mind.SOLUTION: The system includes a task priority setting part, a performance tracking part, and a positive mind forming part. The task priority setting unit analyzes a task of a user and sets a priority. The performance tracking unit tracks the progress of the task set by the task priority setting unit. A positive mind former analyzes the user's emotions and provides positive messages based on the progress tracked by the performance tracker.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately manage users' tasks or track their progress, posing challenges in maintaining user motivation.

[0005] The system according to the embodiment aims to help users manage their tasks and track their progress, fostering a positive mindset. [Means for solving the problem]

[0006] The system according to the embodiment includes a task priority setting unit, a performance tracking unit, and a positive mindset building unit. The task priority setting unit analyzes a user's tasks and sets priorities. The performance tracking unit tracks the progress of the tasks set by the task priority setting unit. The positive mindset building unit analyzes the user's emotions and provides positive messages based on the progress tracked by the performance tracking unit. [Effects of the Invention]

[0007] The system according to the embodiment can manage a user's tasks, track progress, and foster a positive mindset. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​application according to the embodiment of the present invention is a system that provides various functions to help users get a head start in the morning and supports goal achievement. As a result, the AI ​​application can effectively support users' morning activities and help them achieve their goals.

[0029] An AI app according to an embodiment includes a task priority setting unit, a performance tracking unit, and a positive mindset building unit. The task priority setting unit analyzes a user's tasks and sets priorities. For example, the generation AI analyzes tasks entered by the user and sets priorities based on importance and urgency. The generation AI can also learn the user's past task history and set priorities optimized for each individual user. Furthermore, the generation AI can suggest tasks at optimal times by taking into account the user's physiological data (e.g., heart rate and sleep data). The performance tracking unit tracks the user's performance and visualizes progress. For example, the generation AI records the user's completed tasks and ongoing tasks and evaluates performance based on that data. The generation AI can also analyze the user's performance data and provide optimal feedback to each individual user. Furthermore, the generation AI can visualize the performance tracking results as the user's progress toward achieving their long-term goals, thereby maintaining motivation. The positive mindset building unit builds a positive mindset in the user through dialogue with the generation AI. For example, the generation AI analyzes the user's mood and emotions and provides positive messages and advice. The generation AI can also learn from a user's past conversation data and generate optimal positive messages for each individual user. Furthermore, the generation AI can provide customized advice based on the user's hobbies and interests. This allows the AI ​​app according to the embodiment to integrate task management and emotional care for the user. For example, the user can work efficiently by prioritizing tasks and understand progress by tracking performance. Furthermore, cultivating a positive mindset can help users maintain motivation and move toward their goals.

[0030] The task priority setting unit can learn the user's past task history and set priorities that are optimized for each individual user. For example, the generation AI analyzes the user's past task history and sets priorities based on frequency and completion time. For example, tasks that have been performed frequently in the past or tasks that have a short completion time are given priority. The generation AI can also learn the user's behavioral patterns and set optimal priorities. This allows optimal priorities to be set based on the user's past task history.

[0031] The task priority setting unit can suggest tasks at optimal times based on the user's physiological data. For example, the generation AI analyzes the user's heart rate data and suggests important tasks when stress levels are low. For example, tasks that require concentration can be set for times when the heart rate is stable. The generation AI can also analyze the user's sleep data and suggest important tasks after sufficient rest. This allows tasks to be suggested at optimal times based on the user's physiological data.

[0032] The performance tracking unit can analyze user performance data and provide optimal feedback to individual users. For example, the generation AI analyzes user performance data and provides optimal feedback based on task completion time and frequency. For example, if the task completion time is short, it can suggest an efficient work method. The generation AI can also provide feedback including areas for improvement and praise for success based on the user's performance data. This makes it possible to provide optimal feedback based on the user's performance data.

[0033] The performance tracking unit can visualize the performance tracking results as progress toward achieving the user's long-term goal, thereby maintaining motivation. The performance tracking unit, for example, visualizes the performance tracking results in graphs and charts to display the user's progress toward achieving the long-term goal. For example, it displays the progress toward goal achievement in a graph. The generation AI can also update the progress in real time based on the user's performance data and provide feedback to maintain motivation. This makes it possible to visualize the user's progress toward achieving the long-term goal and maintain motivation.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The task priority setting unit can also use the user's geographic data to suggest an optimal task order. For example, it can prioritize tasks that the user can complete while traveling. Also, if the user is in a specific location, it can prioritize tasks that can only be completed in that location. Furthermore, it can learn the user's movement patterns and suggest an efficient task order. This makes it possible to suggest an optimal task order based on the user's geographic data.

[0036] The performance tracking unit can also analyze a user's social media activity and provide feedback that helps improve performance. For example, it can analyze the progress of tasks the user has shared on social media and provide advice based on feedback from other users. It can also analyze the time spent on social media and provide suggestions for efficient time management. It can also provide motivational feedback based on the user's successful experiences on social media. This makes it possible to support performance improvement based on the user's social media activity.

[0037] The task priority setting unit can also analyze the user's calendar data and set task priorities based on the schedule. For example, it can suggest tasks suitable for before or after meetings or events based on the user's calendar. It can also prioritize tasks that can be completed in a short time by using free time on the calendar. It can also learn the user's calendar patterns and support efficient schedule management. This allows it to set optimal task priorities based on the user's calendar data.

[0038] The performance tracking unit can analyze the user's dietary data and make dietary suggestions that will affect performance. For example, it can suggest a meal containing appropriate nutrients before the user performs a task that requires energy. It can also suggest meal timings that will help improve performance based on the user's dietary data. Furthermore, it can learn from the user's past dietary data and provide an optimal diet plan for each individual user. This can support performance improvement based on the user's dietary data.

[0039] The task priority setting unit can also analyze the user's sleep data and suggest an optimal task order. For example, after the user has had enough sleep, it can prioritize tasks that require concentration. Also, if the user is sleep-deprived, it can prioritize lighter tasks. Furthermore, it can learn the user's sleep patterns and suggest an efficient task order. This makes it possible to suggest an optimal task order based on the user's sleep data.

[0040] The performance tracking unit can analyze the user's reading data and suggest books that will help improve performance. For example, if the user wants to improve their concentration, it can suggest books that will help improve concentration. If the user wants to relax, it can suggest books that will help them relax. It can also learn the user's past reading history and suggest books that are best suited to each individual user. This makes it possible to support performance improvement based on the user's reading data.

[0041] The processing flow of the first embodiment will be briefly explained below.

[0042] Step 1: The task priority setting unit analyzes the user's tasks and sets priorities. For example, the generation AI analyzes tasks entered by the user and sets priorities based on importance and urgency. The generation AI can also learn the user's past task history and set priorities optimized for each individual user. Furthermore, the generation AI can take into account the user's physiological data (e.g., heart rate and sleep data) and suggest tasks at the optimal time. Step 2: The performance tracking unit tracks the user's performance and visualizes their progress. For example, the generation AI records the tasks the user has completed and the tasks in progress, and evaluates their performance based on that data. The generation AI can also analyze the user's performance data and provide optimal feedback to each individual user. Furthermore, the generation AI can visualize the performance tracking results as progress toward achieving the user's long-term goals, helping to maintain motivation. Step 3: The positive mindset creation unit creates a positive mindset in the user through dialogue with the generation AI. For example, the generation AI analyzes the user's mood and emotions and provides positive messages and advice. The generation AI can also learn from the user's past dialogue data and generate positive messages that are optimal for each individual user. Furthermore, the generation AI can provide customized advice based on the user's hobbies and interests.

[0043] (Example 2) The AI ​​application according to the embodiment of the present invention is a system that provides various functions to help users get a head start in the morning and supports goal achievement. As a result, the AI ​​application can effectively support users' morning activities and help them achieve their goals.

[0044] An AI app according to an embodiment includes a task priority setting unit, a performance tracking unit, and a positive mindset building unit. The task priority setting unit analyzes a user's tasks and sets priorities. For example, the generation AI analyzes tasks entered by the user and sets priorities based on importance and urgency. The generation AI can also learn the user's past task history and set priorities optimized for each individual user. Furthermore, the generation AI can suggest tasks at optimal times by taking into account the user's physiological data (e.g., heart rate and sleep data). The performance tracking unit tracks the user's performance and visualizes progress. For example, the generation AI records the user's completed tasks and ongoing tasks and evaluates performance based on that data. The generation AI can also analyze the user's performance data and provide optimal feedback to each individual user. Furthermore, the generation AI can visualize the performance tracking results as the user's progress toward achieving their long-term goals, thereby maintaining motivation. The positive mindset building unit builds a positive mindset in the user through dialogue with the generation AI. For example, the generation AI analyzes the user's mood and emotions and provides positive messages and advice. The generation AI can also learn from a user's past conversation data and generate optimal positive messages for each individual user. Furthermore, the generation AI can provide customized advice based on the user's hobbies and interests. This allows the AI ​​app according to the embodiment to integrate task management and emotional care for the user. For example, the user can work efficiently by prioritizing tasks and understand progress by tracking performance. Furthermore, cultivating a positive mindset can help users maintain motivation and move toward their goals.

[0045] The task priority setting unit can learn the user's past task history and set priorities that are optimized for each individual user. For example, the generation AI analyzes the user's past task history and sets priorities based on frequency and completion time. For example, tasks that have been performed frequently in the past or tasks that have a short completion time are given priority. The generation AI can also learn the user's behavioral patterns and set optimal priorities. This allows optimal priorities to be set based on the user's past task history.

[0046] The task priority setting unit can suggest tasks at optimal times based on the user's physiological data. For example, the generation AI analyzes the user's heart rate data and suggests important tasks when stress levels are low. For example, tasks that require concentration can be set for times when the heart rate is stable. The generation AI can also analyze the user's sleep data and suggest important tasks after sufficient rest. This allows tasks to be suggested at optimal times based on the user's physiological data.

[0047] The task priority setting unit can use the emotion estimation function to dynamically change task priorities based on the user's emotional state, thereby reducing stress. For example, the task priority setting unit can use the emotion estimation function to preferentially suggest relaxing tasks when the user is feeling stressed. For example, the task priority setting unit can prioritize relaxing tasks. Furthermore, the emotion estimation function can also be used to suggest challenging tasks when the user is feeling positive emotions. This allows the task priority to be dynamically changed based on the user's emotional state, thereby reducing stress.

[0048] The performance tracking unit can analyze user performance data and provide optimal feedback to individual users. For example, the generation AI analyzes user performance data and provides optimal feedback based on task completion time and frequency. For example, if the task completion time is short, it can suggest an efficient work method. The generation AI can also provide feedback including areas for improvement and praise for success based on the user's performance data. This makes it possible to provide optimal feedback based on the user's performance data.

[0049] The performance tracking unit can visualize the performance tracking results as progress toward achieving the user's long-term goal, thereby maintaining motivation. The performance tracking unit, for example, visualizes the performance tracking results in graphs and charts to display the user's progress toward achieving the long-term goal. For example, it displays the progress toward goal achievement in a graph. The generation AI can also update the progress in real time based on the user's performance data and provide feedback to maintain motivation. This makes it possible to visualize the user's progress toward achieving the long-term goal and maintain motivation.

[0050] The performance tracking unit can use the emotion estimation function to adjust the performance evaluation based on the user's emotional state and emphasize positive feedback. For example, the performance tracking unit can use the emotion estimation function to adjust the performance evaluation based on the user's emotional state and emphasize positive feedback. For example, if the user has strong positive emotions, the performance tracking unit can highly evaluate the achievement. The emotion estimation function can also be used to provide encouraging feedback if the user has negative emotions. This allows the performance evaluation to be adjusted based on the user's emotional state and emphasize positive feedback.

[0051] The positive mindset creation unit can use the generation AI to learn the user's past dialogue data and generate the optimal positive message for each individual user. For example, the generation AI analyzes the user's past dialogue data and generates a positive message. For example, the message can be created based on content that the user found enjoyable in past dialogues. The generation AI can also generate positive messages that include words of encouragement or sharing of successful experiences based on the user's dialogue data. This allows the optimal positive message to be generated based on the user's past dialogue data.

[0052] The positive mindset creation unit can use the generation AI to provide customized advice based on the user's hobbies or interests. For example, the generation AI analyzes the user's hobbies and interests and provides customized advice based on them. For example, the generation AI can provide advice related to the user's favorite music or movies. The generation AI can also provide individual suggestions and personalized messages based on the user's hobbies and interests. This makes it possible to provide customized advice based on the user's hobbies and interests.

[0053] The positive mindset creation unit can use the emotion estimation function to analyze the user's emotional state in real time and provide a positive message at the optimal timing. The positive mindset creation unit can, for example, use the emotion estimation function to analyze the user's emotional state in real time and provide a positive message at the optimal timing. For example, it can provide an encouraging message when the user is feeling down. It can also use the emotion estimation function to provide a congratulatory message when the user is happy. This allows the user's emotional state to be analyzed in real time and a positive message to be provided at the optimal timing.

[0054] The positive mindset creation unit can use the generation AI to analyze conversation data between the user and friends and family and provide advice to strengthen relationships. For example, the generation AI can analyze conversation data between the user and friends and family and provide advice to strengthen relationships. For example, it can provide advice regarding specific events or anniversaries. The generation AI can also provide advice based on the user's conversation data, including suggestions for improving communication and common hobbies. This makes it possible to provide advice to strengthen relationships with the user's friends and family.

[0055] The positive mindset creation unit may provide positive messages through voice or lighting in cooperation with the user's smart home devices. For example, the positive mindset creation unit may provide positive messages through voice in cooperation with the user's smart home devices. For example, it may play encouraging messages through a smart speaker. The positive mindset creation unit may also provide positive messages through color changes or brightness adjustments in cooperation with smart lighting. In this way, positive messages can be provided in cooperation with smart home devices.

[0056] The positive mindset creation unit can use the emotion estimation function to analyze the emotion a user feels when receiving a positive message and select the optimal message format. The positive mindset creation unit can, for example, use the emotion estimation function to analyze the emotion a user feels when receiving a positive message and select the optimal message format. For example, when the user is happy, a congratulatory message can be provided. The emotion estimation function can also be used to provide an encouraging message when the user is depressed. This allows the emotion a user feels when receiving a positive message to be analyzed and the optimal message format to be selected.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The task priority setting unit can also use the user's geographic data to suggest an optimal task order. For example, it can prioritize tasks that the user can complete while traveling. Also, if the user is in a specific location, it can prioritize tasks that can only be completed in that location. Furthermore, it can learn the user's movement patterns and suggest an efficient task order. This makes it possible to suggest an optimal task order based on the user's geographic data.

[0059] The performance tracking unit can also analyze a user's social media activity and provide feedback that helps improve performance. For example, it can analyze the progress of tasks the user has shared on social media and provide advice based on feedback from other users. It can also analyze the time spent on social media and provide suggestions for efficient time management. It can also provide motivational feedback based on the user's successful experiences on social media. This makes it possible to support performance improvement based on the user's social media activity.

[0060] The positive mind creation unit can also analyze the user's music playlist and suggest music that matches their emotional state. For example, when the user wants to relax, it can suggest relaxing music. When the user wants to concentrate, it can suggest music that will help them concentrate. Furthermore, it can learn the user's past music playback history and suggest music that is best suited to each individual user. This makes it possible to suggest music that matches the user's emotional state based on the user's music playlist.

[0061] The task priority setting unit can also analyze the user's calendar data and set task priorities based on the schedule. For example, it can suggest tasks suitable for before or after meetings or events based on the user's calendar. It can also prioritize tasks that can be completed in a short time by using free time on the calendar. It can also learn the user's calendar patterns and support efficient schedule management. This allows it to set optimal task priorities based on the user's calendar data.

[0062] The performance tracking unit can analyze the user's dietary data and make dietary suggestions that will affect performance. For example, it can suggest a meal containing appropriate nutrients before the user performs a task that requires energy. It can also suggest meal timings that will help improve performance based on the user's dietary data. Furthermore, it can learn from the user's past dietary data and provide an optimal diet plan for each individual user. This can support performance improvement based on the user's dietary data.

[0063] The positive mind formation unit can also suggest relaxation techniques based on the user's emotional state. For example, if the user is feeling stressed, it can suggest deep breathing or meditation techniques. Also, if the user wants to relax, it can suggest yoga or stretching techniques. Furthermore, it can learn the user's past use history of relaxation techniques and suggest the most suitable relaxation technique for each individual user. This makes it possible to suggest relaxation techniques based on the user's emotional state.

[0064] The task priority setting unit can also analyze the user's sleep data and suggest an optimal task order. For example, after the user has had enough sleep, it can prioritize tasks that require concentration. Also, if the user is sleep-deprived, it can prioritize lighter tasks. Furthermore, it can learn the user's sleep patterns and suggest an efficient task order. This makes it possible to suggest an optimal task order based on the user's sleep data.

[0065] The positive mind formation unit can also suggest appropriate exercises based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user needs energy, it can suggest invigorating exercises. Furthermore, it can learn the user's past exercise history and suggest exercises that are optimal for each individual user. This makes it possible to suggest appropriate exercises based on the user's emotional state.

[0066] The performance tracking unit can analyze the user's reading data and suggest books that will help improve performance. For example, if the user wants to improve their concentration, it can suggest books that will help improve concentration. If the user wants to relax, it can suggest books that will help them relax. It can also learn the user's past reading history and suggest books that are best suited to each individual user. This makes it possible to support performance improvement based on the user's reading data.

[0067] The positive mind formation unit can also suggest appropriate resting methods based on the user's emotional state. For example, if the user is tired, it can suggest a short break or a nap. Also, if the user is feeling stressed, it can suggest a relaxing resting method. Furthermore, it can learn the user's past resting history and suggest the optimal resting method for each individual user. This makes it possible to suggest appropriate resting methods based on the user's emotional state.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The task priority setting unit analyzes the user's tasks and sets priorities. For example, the generation AI analyzes tasks entered by the user and sets priorities based on importance and urgency. The generation AI can also learn the user's past task history and set priorities optimized for each individual user. Furthermore, the generation AI can take into account the user's physiological data (e.g., heart rate and sleep data) and suggest tasks at the optimal time. Step 2: The performance tracking unit tracks the user's performance and visualizes their progress. For example, the generation AI records the tasks the user has completed and the tasks in progress, and evaluates their performance based on that data. The generation AI can also analyze the user's performance data and provide optimal feedback to each individual user. Furthermore, the generation AI can visualize the performance tracking results as progress toward achieving the user's long-term goals, helping to maintain motivation. Step 3: The positive mindset creation unit creates a positive mindset in the user through dialogue with the generation AI. For example, the generation AI analyzes the user's mood and emotions and provides positive messages and advice. The generation AI can also learn from the user's past dialogue data and generate positive messages that are optimal for each individual user. Furthermore, the generation AI can provide customized advice based on the user's hobbies and interests.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

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

[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 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.

[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 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.

[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 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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]

[0137] 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. The system includes a task priority setting unit that analyzes a user's tasks and sets priorities, a performance tracking unit that tracks the progress of the tasks set by the task priority setting unit, and a positive mind-building unit that analyzes the user's emotions and provides positive messages based on the progress tracked by the performance tracking unit. A system characterized by:

2. The task priority setting unit Learns the user's past task history and sets priorities optimized for each individual user 2. The system of claim 1.

3. The performance tracking unit: Analyzing the performance data of the user and providing optimal feedback to each individual user 2. The system of claim 1.

4. The positive mindset development unit: Generative AI learns from the user's past conversation data and generates optimal positive messages for each individual user.

2. The system of claim 1.

5. The task priority setting unit Using emotion estimation functionality, the prioritization of the tasks is dynamically changed based on the user's emotional state to reduce stress.

2. The system of claim 1.

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