System

Abstract

An object of a system according to an embodiment is to appropriately prioritize dreams, desires, and future plans of a user and to timely remind the user.SOLUTION: A system includes a user information input unit, an analysis unit, a priority setting unit, and a reminder unit. The user information input unit inputs information on the user's dreams, desires, things to do in the future, and things not to do in the future. The analysis unit analyzes the user information input by the user information input unit. The priority setting unit sets the priority based on the user information analyzed by the analysis unit. The reminder unit transmits a reminder on the basis of the priority set by the priority setting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately prioritize users' dreams, hopes, and future plans and provide timely reminders.

[0005] The system according to the embodiment aims to appropriately prioritize the user's dreams, hopes, and future plans and to provide timely reminders. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, an analysis unit, a priority setting unit, and a reminder unit. The user information input unit inputs information about the user's dreams, hopes, future goals, and future goals. The analysis unit analyzes the user information input by the user information input unit. The priority setting unit sets priorities based on the user information analyzed by the analysis unit. The reminder unit sends reminders based on the priorities set by the priority setting unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately prioritize the user's dreams, hopes, and future plans and remind them in a timely manner. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 smartphone app service according to the embodiment of the present invention is a system that appropriately prioritizes the user's dreams, hopes, future goals, and future goals that the user does not want to achieve, and provides assistance to the user at the appropriate time. This allows the smartphone app service to efficiently manage the user's goals and hopes and provide support at the appropriate time.

[0029] A smartphone app service according to an embodiment includes a user information input unit, an analysis unit, a priority setting unit, and a reminder unit. The user information input unit inputs information about the user's dreams, hopes, future goals, and future goals. For example, the user may input information such as "I want to be a doctor in the future," "I want to master English by next year," "I want to continue exercising every day," and "I absolutely do not want to stay up late at night." The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to understand the user's goals and hopes. The generation AI can also perform sentiment analysis to analyze the user's emotions. The priority setting unit sets priorities based on the user information analyzed by the analysis unit. For example, the generation AI suggests the optimal order for the user to achieve their goals. The reminder unit sends reminders based on the priorities set by the priority setting unit. For example, the generation AI takes into account the user's schedule and progress and sends reminders at the optimal time. As a result, the smartphone app service according to the embodiment can efficiently manage the user's goals and wishes and provide support at the appropriate time.

[0030] The analysis unit can analyze the user's past behavioral history or achievement level based on user information and reflect this in future goal setting. For example, if a user previously inputs a goal such as "I want to continue exercising every day," the generation AI analyzes the user's past exercise history and evaluates the level of achievement. For example, if the user exercises frequently, the generation AI can prioritize exercise when setting future goals. Similarly, if a user inputs "I want to master English by next year," the analysis unit analyzes the user's past learning history and evaluates the level of achievement. For example, if the user has little time to study, the generation AI can prioritize learning when setting future goals. Similarly, if a user inputs "I absolutely do not want to stay up late," the generation AI can analyze the user's past sleep history and evaluate the level of achievement. For example, if the user frequently stays up late, the generation AI can prioritize sleep when setting future goals. This allows for more realistic goal setting by taking into account the user's past behavioral history and achievement level.

[0031] The user information input unit can use voice input or image recognition to enable input in a wider variety of data formats. For example, if a user inputs "I want to be a doctor in the future" by voice, the generation AI uses voice recognition technology to convert it into text data and analyze it. For example, noise canceling technology is used to improve the accuracy of voice input. Furthermore, if a user inputs "I want to continue exercising every day" by image, the generation AI uses image recognition technology to analyze the type and frequency of exercise. For example, photos and videos of the exercise are analyzed and details of the exercise are registered in a database. Furthermore, if a user inputs "I never want to stay up late" by voice, the generation AI uses voice recognition technology to convert it into text data and analyze it. For example, emotion analysis is performed during voice input to evaluate the user's emotional state. This allows for improved user convenience by using voice input and image recognition.

[0032] The analysis unit can perform multilingual analysis to accommodate users from different cultures and regions. For example, if a user inputs "I want to be a doctor in the future" in Japanese, the generation AI performs multilingual analysis and translates the input into other languages ​​such as English or Chinese for analysis. For example, machine translation technology is used to improve translation accuracy. Similarly, if a user inputs "I want to exercise every day" in Spanish, the generation AI performs multilingual analysis to analyze the type and frequency of exercise. For example, details of the exercise are registered in a database in multiple languages. Similarly, if a user inputs "I never want to stay up late" in French, the generation AI performs multilingual analysis and converts the input into text data for analysis. For example, sentiment analysis is performed in multiple languages ​​to evaluate the user's emotional state. This multilingual analysis allows the system to accommodate users from different cultures and regions.

[0033] The priority setting unit can dynamically reset priorities taking into account changes in the user's life events or environment. For example, if a user inputs "I want to be a doctor in the future" and then inputs "I passed the university entrance exam" as a life event, the generation AI dynamically resets priorities. For example, the priority setting unit sets "Pass the national medical examination" as the next goal. Also, if a user inputs "I want to continue exercising every day" and then inputs "I moved" as an environmental change, the generation AI dynamically resets priorities. For example, the priority setting unit suggests an exercise plan to adapt to the new environment. Also, if a user inputs "I absolutely do not want to stay up late" and then inputs "I started a new job" as a life event, the generation AI dynamically resets priorities. For example, the priority setting unit suggests a sleep plan to adapt to the new job. This allows the user to achieve their goals by dynamically resetting priorities in response to changes in life events and environment.

[0034] The priority setting unit can set priorities taking into account the user's health condition or fitness data. For example, when a user inputs, "I want to continue exercising every day," the generating AI takes the user's health condition and fitness data into account to set priorities. For example, the priority setting unit may set a high priority for exercise based on heart rate and exercise volume. Also, when a user inputs, "I absolutely do not want to stay up late," the generating AI takes the user's health condition and fitness data into account to set priorities. For example, the priority setting unit may set a high priority for sleep based on sleep quality and sleep duration. Also, when a user inputs, "I want to be a doctor in the future," the generating AI takes the user's health condition and fitness data into account to set priorities. For example, the priority setting unit may set a high priority for studying based on stress level and health condition. In this way, by taking into account the user's health condition and fitness data, it is possible to support the user in achieving their goals while maintaining their health.

[0035] The priority setting unit can set customized priorities for different user groups. For example, when a user inputs, "I want to be a doctor in the future," the generation AI takes into account the user's attributes (e.g., student) and sets customized priorities. For example, it may set a high priority for studying. Also, when a user inputs, "I want to continue exercising every day," the priority setting unit takes into account the user's attributes (e.g., working adult) and sets customized priorities. For example, it may suggest a plan to incorporate exercise into work breaks. Also, when a user inputs, "I absolutely do not want to stay up late," the priority setting unit takes into account the user's attributes (e.g., senior citizen) and sets customized priorities. For example, it may suggest a plan to improve sleep quality. In this way, by setting customized priorities for different user groups, it is possible to provide support tailored to the needs of each user.

[0036] The reminder unit monitors the user's schedule or progress in real time and can provide assistance at the optimal time. For example, when a user inputs, "I want to continue exercising every day," the generation AI monitors the user's schedule and progress in real time and provides exercise assistance at the optimal time. For example, it may remind the user to exercise. Alternatively, when a user inputs, "I absolutely do not want to stay up late," the generation AI monitors the user's schedule and progress in real time and provides sleep assistance at the optimal time. For example, it may send a bedtime reminder. Alternatively, when a user inputs, "I want to be a doctor in the future," the generation AI monitors the user's schedule and progress in real time and provides study assistance at the optimal time. For example, it may remind the user to study. This allows the system to support the user in achieving their goals by monitoring the schedule and progress in real time and providing assistance at the optimal time.

[0037] The reminder unit can customize the content of the reminder according to the user's preferences to provide more personalized assistance. For example, when a user inputs, "I want to continue exercising every day," the generation AI customizes the content of the reminder according to the user's preferences to provide personalized assistance. For example, the reminder unit incorporates the user's favorite music into the reminder. Also, when a user inputs, "I absolutely do not want to stay up late," the reminder unit customizes the content of the reminder according to the user's preferences to provide personalized assistance. For example, the reminder unit incorporates music that the user finds relaxing into the reminder. Also, when a user inputs, "I want to be a doctor in the future," the generation AI customizes the content of the reminder according to the user's preferences to provide personalized assistance. For example, the reminder unit incorporates the user's favorite motivational video into the reminder. This allows the reminder content to be customized according to the user's preferences, thereby providing more effective assistance.

[0038] The reminder unit can link with different devices to realize multi-device reminders. For example, when a user inputs "I want to continue exercising every day," the generation AI links with a smartwatch to send an exercise reminder. For example, the smartwatch vibrates to notify when it's time to exercise. Alternatively, when a user inputs "I absolutely don't want to stay up late," the generation AI links with a smart speaker to send a sleep reminder. For example, the smart speaker plays relaxing music before bedtime. Alternatively, when a user inputs "I want to be a doctor in the future," the generation AI links with a smartphone to send a study reminder. For example, the smartphone displays a notification when it's time to study. This allows for linking with different devices to improve user convenience.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The user information input unit can use voice input or image recognition to enable input in a wider variety of data formats. For example, if a user inputs "I want to be a doctor in the future," the generation AI uses voice recognition technology to convert the input into text data and analyze it. For example, noise cancellation technology can be used to improve the accuracy of voice input. Furthermore, if a user inputs "I want to continue exercising every day" using an image, the generation AI can use image recognition technology to analyze the type and frequency of exercise. For example, it can analyze photos and videos of the exercise and register the details of the exercise in a database. Furthermore, if a user inputs "I never want to stay up late," the generation AI can use voice recognition technology to convert the input into text data and analyze it. For example, it can perform emotion analysis during voice input to evaluate the user's emotional state. This allows for improved user convenience through the use of voice input and image recognition.

[0041] The analysis unit can perform multilingual analysis to accommodate users from different cultures and regions. For example, if a user inputs "I want to be a doctor in the future" in Japanese, the generation AI performs multilingual analysis and translates the input into other languages ​​such as English or Chinese for analysis. For example, machine translation technology is used to improve translation accuracy. Similarly, if a user inputs "I want to continue exercising every day" in Spanish, the generation AI performs multilingual analysis to analyze the type and frequency of exercise. For example, details of the exercise are registered in a database in multiple languages. Similarly, if a user inputs "I never want to stay up late" in French, the generation AI performs multilingual analysis and converts the input into text data for analysis. For example, sentiment analysis is performed in multiple languages ​​to evaluate the user's emotional state. This multilingual analysis allows the system to accommodate users from different cultures and regions.

[0042] The priority setting unit can dynamically reset priorities taking into account changes in the user's life events or environment. For example, if a user inputs "I want to be a doctor in the future" and then inputs "I passed the university entrance exam" as a life event, the generation AI dynamically resets priorities. For example, it sets "Pass the national medical examination" as the next goal. Furthermore, if a user inputs "I want to continue exercising every day" and then inputs "I moved" as an environmental change, the generation AI dynamically resets priorities. For example, it proposes an exercise plan to adapt to the new environment. Furthermore, if a user inputs "I absolutely do not want to stay up late" and then inputs "I started a new job" as a life event, the generation AI dynamically resets priorities. For example, it proposes a sleep plan to adapt to the new job. This allows the generation AI to support the user in achieving their goals by dynamically resetting priorities in response to changes in life events and environment.

[0043] The reminder unit monitors the user's schedule or progress in real time and can provide assistance at the optimal time. For example, when a user inputs, "I want to continue exercising every day," the generation AI monitors the user's schedule and progress in real time and provides exercise assistance at the optimal time. For example, it may remind the user to exercise. Similarly, when a user inputs, "I absolutely do not want to stay up late," the reminder unit monitors the user's schedule and progress in real time and provides sleep assistance at the optimal time. For example, it may send a bedtime reminder. Similarly, when a user inputs, "I want to be a doctor in the future," the reminder unit monitors the user's schedule and progress in real time and provides study assistance at the optimal time. For example, it may remind the user to study. This allows the system to support the user in achieving their goals by monitoring the schedule and progress in real time and providing assistance at the optimal time.

[0044] The reminder unit can link with different devices to realize multi-device reminders. For example, when a user inputs "I want to continue exercising every day," the generation AI links with a smartwatch to send an exercise reminder. For example, the smartwatch will vibrate to notify when it's time to exercise. Also, when a user inputs "I absolutely don't want to stay up late," the reminder unit links with a smart speaker to send a sleep reminder. For example, the smart speaker will play relaxing music before bedtime. Also, when a user inputs "I want to be a doctor in the future," the generation AI links with a smartphone to send a study reminder. For example, the smartphone will display a notification when it's time to study. This allows for linking with different devices to improve user convenience.

[0045] The analysis unit can analyze a user's past behavioral history or achievement level based on user information and reflect this in future goal setting. For example, if a user previously inputs a goal such as "I want to continue exercising every day," the generation AI analyzes the user's past exercise history and evaluates the level of achievement. For example, if the user exercises frequently, the generation AI can prioritize exercise in future goal setting. Similarly, if a user inputs "I want to master English by next year," the analysis unit analyzes the user's past learning history and evaluates the level of achievement. For example, if the user has little time to study, the generation AI can prioritize learning in future goal setting. Similarly, if a user inputs "I absolutely do not want to stay up late," the generation AI can analyze the user's past sleep history and evaluate the level of achievement. For example, if the user frequently stays up late, the generation AI can prioritize sleep in future goal setting. This allows for more realistic goal setting by taking into account past behavioral history and achievement level.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The user information input section inputs information about the user's dreams, hopes, future goals, and future goals. For example, a user can input information such as "I want to be a doctor in the future," "I want to master English by next year," "I want to continue exercising every day," and "I absolutely do not want to stay up late at night." Step 2: The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to understand the user's goals and wishes. The generation AI can also perform sentiment analysis to analyze the user's emotions. Step 3: The priority setting unit sets priorities based on the user information analyzed by the analysis unit. For example, the generation AI suggests the optimal order for achieving the user's goals. Step 4: The reminder unit sends reminders based on the priorities set by the priority setting unit. For example, the generation AI takes into account the user's schedule and progress and sends reminders at the optimal time.

[0048] (Example 2) The smartphone app service according to the embodiment of the present invention is a system that appropriately prioritizes the user's dreams, hopes, future goals, and future goals that the user does not want to achieve, and provides assistance to the user at the appropriate time. This allows the smartphone app service to efficiently manage the user's goals and hopes and provide support at the appropriate time.

[0049] A smartphone app service according to an embodiment includes a user information input unit, an analysis unit, a priority setting unit, and a reminder unit. The user information input unit inputs information about the user's dreams, hopes, future goals, and future goals. For example, the user may input information such as "I want to be a doctor in the future," "I want to master English by next year," "I want to continue exercising every day," and "I absolutely do not want to stay up late at night." The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to understand the user's goals and hopes. The generation AI can also perform sentiment analysis to analyze the user's emotions. The priority setting unit sets priorities based on the user information analyzed by the analysis unit. For example, the generation AI suggests the optimal order for the user to achieve their goals. The reminder unit sends reminders based on the priorities set by the priority setting unit. For example, the generation AI takes into account the user's schedule and progress and sends reminders at the optimal time. As a result, the smartphone app service according to the embodiment can efficiently manage the user's goals and wishes and provide support at the appropriate time.

[0050] The analysis unit performs emotional analysis on user information and can adjust goal priorities based on the intensity or type of emotion. For example, when a user inputs information like "I want to be a doctor in the future," the generation AI performs emotional analysis and quantifies the user's passion and motivation. For example, if the emotional score is high, the generation AI performs emotional analysis to evaluate the strength of the user's motivation. For example, if the emotional score is low, the generation AI provides feedback to increase motivation. For example, when a user inputs "I absolutely do not want to stay up late," the analysis unit performs emotional analysis to evaluate the user's stress and anxiety. For example, if the emotional score is high, the generation AI sets the priority of that goal high and provides appropriate assistance. This allows for more effective support by adjusting goal priorities based on the user's emotions.

[0051] The analysis unit can analyze the user's past behavioral history or achievement level based on user information and reflect this in future goal setting. For example, if a user previously inputs a goal such as "I want to continue exercising every day," the generation AI analyzes the user's past exercise history and evaluates the level of achievement. For example, if the user exercises frequently, the generation AI can prioritize exercise when setting future goals. Similarly, if a user inputs "I want to master English by next year," the analysis unit analyzes the user's past learning history and evaluates the level of achievement. For example, if the user has little time to study, the generation AI can prioritize learning when setting future goals. Similarly, if a user inputs "I absolutely do not want to stay up late," the generation AI can analyze the user's past sleep history and evaluate the level of achievement. For example, if the user frequently stays up late, the generation AI can prioritize sleep when setting future goals. This allows for more realistic goal setting by taking into account the user's past behavioral history and achievement level.

[0052] The analysis unit can infer emotions when analyzing user information and provide feedback to elicit positive emotions. For example, when a user inputs, "I want to be a doctor in the future," the analysis unit allows the generation AI to infer emotions and provide feedback to elicit positive emotions. For example, it displays a message such as, "We support your dream!". Also, when a user inputs, "I want to continue exercising every day," the analysis unit allows the generation AI to infer emotions and provide feedback to elicit positive emotions. For example, it displays a message such as, "That's a great goal! Let's work hard together!". Also, when a user inputs, "I absolutely don't want to stay up late," the analysis unit allows the generation AI to infer emotions and provide feedback to elicit positive emotions. For example, it displays a message such as, "Let's work hard to live a healthy life!". This elicits positive emotions and increases the user's motivation.

[0053] The user information input unit can use voice input or image recognition to enable input in a wider variety of data formats. For example, if a user inputs "I want to be a doctor in the future" by voice, the generation AI uses voice recognition technology to convert it into text data and analyze it. For example, noise canceling technology is used to improve the accuracy of voice input. Furthermore, if a user inputs "I want to continue exercising every day" by image, the generation AI uses image recognition technology to analyze the type and frequency of exercise. For example, photos and videos of the exercise are analyzed and details of the exercise are registered in a database. Furthermore, if a user inputs "I never want to stay up late" by voice, the generation AI uses voice recognition technology to convert it into text data and analyze it. For example, emotion analysis is performed during voice input to evaluate the user's emotional state. This allows for improved user convenience by using voice input and image recognition.

[0054] The analysis unit can perform multilingual analysis to accommodate users from different cultures and regions. For example, if a user inputs "I want to be a doctor in the future" in Japanese, the generation AI performs multilingual analysis and translates the input into other languages ​​such as English or Chinese for analysis. For example, machine translation technology is used to improve translation accuracy. Similarly, if a user inputs "I want to exercise every day" in Spanish, the generation AI performs multilingual analysis to analyze the type and frequency of exercise. For example, details of the exercise are registered in a database in multiple languages. Similarly, if a user inputs "I never want to stay up late" in French, the generation AI performs multilingual analysis and converts the input into text data for analysis. For example, sentiment analysis is performed in multiple languages ​​to evaluate the user's emotional state. This multilingual analysis allows the system to accommodate users from different cultures and regions.

[0055] The analysis unit uses the emotion estimation function to analyze the user's emotions in real time as they input information and provide appropriate assistance based on the input content. For example, when a user inputs, "I want to be a doctor in the future," the generation AI uses the emotion estimation function to analyze the user's emotions in real time and provide appropriate assistance. For example, a message such as, "We support your dream!" is displayed. Similarly, when a user inputs, "I want to continue exercising every day," the generation AI uses the emotion estimation function to analyze the user's emotions in real time and provide appropriate assistance. For example, a message such as, "That's a great goal! Let's work hard together!" is displayed. Similarly, when a user inputs, "I absolutely don't want to stay up late," the analysis unit uses the emotion estimation function to analyze the user's emotions in real time and provide appropriate assistance. For example, a message such as, "Let's work hard to live a healthy life!" is displayed. This allows for real-time analysis of emotions and providing appropriate assistance, thereby improving user satisfaction.

[0056] The priority setting unit can analyze emotions based on user information and prioritize emotionally positive goals. For example, when a user inputs "I want to be a doctor in the future," the generation AI analyzes the emotion and prioritizes goals associated with strong positive emotions. For example, if the emotion score is high, the priority of that goal is set high. Also, when a user inputs "I want to continue exercising every day," the priority setting unit analyzes the emotion and prioritizes goals associated with strong positive emotions. For example, if the emotion score is high, the priority of that goal is set high. Also, when a user inputs "I absolutely do not want to stay up late," the priority setting unit analyzes the emotion and prioritizes goals associated with strong positive emotions. For example, if the emotion score is high, the priority of that goal is set high. In this way, by prioritizing emotionally positive goals, the user's motivation can be increased.

[0057] The priority setting unit can dynamically reset priorities taking into account changes in the user's life events or environment. For example, if a user inputs "I want to be a doctor in the future" and then inputs "I passed the university entrance exam" as a life event, the generation AI dynamically resets priorities. For example, the priority setting unit sets "Pass the national medical examination" as the next goal. Also, if a user inputs "I want to continue exercising every day" and then inputs "I moved" as an environmental change, the generation AI dynamically resets priorities. For example, the priority setting unit suggests an exercise plan to adapt to the new environment. Also, if a user inputs "I absolutely do not want to stay up late" and then inputs "I started a new job" as a life event, the generation AI dynamically resets priorities. For example, the priority setting unit suggests a sleep plan to adapt to the new job. This allows the user to achieve their goals by dynamically resetting priorities in response to changes in life events and environment.

[0058] The priority setting unit can use the emotion estimation function to suggest priorities for increasing emotional satisfaction. For example, when a user inputs, "I want to be a doctor in the future," the priority setting unit uses the generation AI to estimate the emotion and suggest priorities for increasing emotional satisfaction. For example, the priority setting unit prioritizes suggesting a "study plan to pass the national medical examination." Furthermore, when a user inputs, "I want to continue exercising every day," the priority setting unit uses the generation AI to estimate the emotion and suggest priorities for increasing emotional satisfaction. For example, the priority setting unit prioritizes suggesting a "plan to increase the types and frequency of exercise." Furthermore, when a user inputs, "I absolutely do not want to stay up late," the priority setting unit uses the generation AI to estimate the emotion and suggest priorities for increasing emotional satisfaction. For example, the priority setting unit prioritizes suggesting a "plan to improve the quality of sleep." In this way, by suggesting priorities for increasing emotional satisfaction, the user's sense of happiness can be improved.

[0059] The priority setting unit can set priorities taking into account the user's health condition or fitness data. For example, when a user inputs, "I want to continue exercising every day," the generating AI takes the user's health condition and fitness data into account to set priorities. For example, the priority setting unit may set a high priority for exercise based on heart rate and exercise volume. Also, when a user inputs, "I absolutely do not want to stay up late," the generating AI takes the user's health condition and fitness data into account to set priorities. For example, the priority setting unit may set a high priority for sleep based on sleep quality and sleep duration. Also, when a user inputs, "I want to be a doctor in the future," the generating AI takes the user's health condition and fitness data into account to set priorities. For example, the priority setting unit may set a high priority for studying based on stress level and health condition. In this way, by taking into account the user's health condition and fitness data, it is possible to support the user in achieving their goals while maintaining their health.

[0060] The priority setting unit can set customized priorities for different user groups. For example, when a user inputs, "I want to be a doctor in the future," the generation AI takes into account the user's attributes (e.g., student) and sets customized priorities. For example, it may set a high priority for studying. Also, when a user inputs, "I want to continue exercising every day," the priority setting unit takes into account the user's attributes (e.g., working adult) and sets customized priorities. For example, it may suggest a plan to incorporate exercise into work breaks. Also, when a user inputs, "I absolutely do not want to stay up late," the priority setting unit takes into account the user's attributes (e.g., senior citizen) and sets customized priorities. For example, it may suggest a plan to improve sleep quality. In this way, by setting customized priorities for different user groups, it is possible to provide support tailored to the needs of each user.

[0061] The priority setting unit uses the emotion estimation function to set priorities based on the user's emotions, allowing the user to maintain emotional balance. For example, when a user inputs, "I want to be a doctor in the future," the generation AI uses the emotion estimation function to analyze the user's emotions and set priorities for maintaining emotional balance. For example, it suggests relaxation time to reduce stress. Furthermore, when a user inputs, "I want to continue exercising every day," the generation AI uses the emotion estimation function to analyze the user's emotions and set priorities for maintaining emotional balance. For example, it suggests a time to refresh after exercise. Furthermore, when a user inputs, "I absolutely do not want to stay up late," the priority setting unit uses the emotion estimation function to analyze the user's emotions and set priorities for maintaining emotional balance. For example, it suggests a relaxing evening routine. This setting of priorities for maintaining emotional balance reduces the user's stress and supports goal achievement.

[0062] The reminder unit can analyze emotions based on user information and send reminders at times when emotions are positive. For example, when a user inputs, "I want to continue exercising every day," the generation AI analyzes the user's emotions and sends reminders when positive emotions are strongest. For example, it sends a reminder to exercise when the user is relaxed. Also, when a user inputs, "I absolutely do not want to stay up late," the generation AI analyzes the user's emotions and sends a reminder when positive emotions are strongest. For example, it sends a reminder to sleep when the user is relaxed. Also, when a user inputs, "I want to be a doctor in the future," the generation AI analyzes the user's emotions and sends a reminder when positive emotions are strongest. For example, it sends a reminder to study when the user is concentrating. This allows the user to be motivated by sending reminders at times when emotions are positive.

[0063] The reminder unit monitors the user's schedule or progress in real time and can provide assistance at the optimal time. For example, when a user inputs, "I want to continue exercising every day," the generation AI monitors the user's schedule and progress in real time and provides exercise assistance at the optimal time. For example, it may remind the user to exercise. Alternatively, when a user inputs, "I absolutely do not want to stay up late," the generation AI monitors the user's schedule and progress in real time and provides sleep assistance at the optimal time. For example, it may send a bedtime reminder. Alternatively, when a user inputs, "I want to be a doctor in the future," the generation AI monitors the user's schedule and progress in real time and provides study assistance at the optimal time. For example, it may remind the user to study. This allows the system to support the user in achieving their goals by monitoring the schedule and progress in real time and providing assistance at the optimal time.

[0064] The reminder unit can use the emotion estimation function to send reminders to enhance emotional motivation. For example, when a user inputs, "I want to continue exercising every day," the generation AI estimates the user's emotion and sends a reminder to enhance emotional motivation. For example, it sends a message such as, "Let's do our best today!". Also, when a user inputs, "I absolutely do not want to stay up late," the generation AI estimates the user's emotion and sends a reminder to enhance emotional motivation. For example, it sends a message such as, "Let's do our best to live a healthy life!". Also, when a user inputs, "I want to be a doctor in the future," the generation AI estimates the user's emotion and sends a reminder to enhance emotional motivation. For example, it sends a message such as, "We support your dream!". In this way, by sending reminders to enhance emotional motivation, it is possible to support the user in achieving their goals.

[0065] The reminder unit can customize the content of the reminder according to the user's preferences to provide more personalized assistance. For example, when a user inputs, "I want to continue exercising every day," the generation AI customizes the content of the reminder according to the user's preferences to provide personalized assistance. For example, the reminder unit incorporates the user's favorite music into the reminder. Also, when a user inputs, "I absolutely do not want to stay up late," the reminder unit customizes the content of the reminder according to the user's preferences to provide personalized assistance. For example, the reminder unit incorporates music that the user finds relaxing into the reminder. Also, when a user inputs, "I want to be a doctor in the future," the generation AI customizes the content of the reminder according to the user's preferences to provide personalized assistance. For example, the reminder unit incorporates the user's favorite motivational video into the reminder. This allows the reminder content to be customized according to the user's preferences, thereby providing more effective assistance.

[0066] The reminder unit can link with different devices to realize multi-device reminders. For example, when a user inputs "I want to continue exercising every day," the generation AI links with a smartwatch to send an exercise reminder. For example, the smartwatch vibrates to notify when it's time to exercise. Alternatively, when a user inputs "I absolutely don't want to stay up late," the generation AI links with a smart speaker to send a sleep reminder. For example, the smart speaker plays relaxing music before bedtime. Alternatively, when a user inputs "I want to be a doctor in the future," the generation AI links with a smartphone to send a study reminder. For example, the smartphone displays a notification when it's time to study. This allows for linking with different devices to improve user convenience.

[0067] The reminder unit uses the emotion estimation function to adjust the content or timing of reminders according to the user's emotions, thereby enhancing emotional support. For example, when a user inputs, "I want to continue exercising every day," the generation AI uses the emotion estimation function to analyze the user's emotions and adjust the content and timing of reminders to enhance emotional support. For example, the generation AI sends an exercise reminder during a time when the user is relaxed. Also, when a user inputs, "I absolutely do not want to stay up late," the generation AI uses the emotion estimation function to analyze the user's emotions and adjust the content and timing of reminders to enhance emotional support. For example, the generation AI sends a sleep reminder during the evening hours when the user is relaxing. Also, when a user inputs, "I want to be a doctor in the future," the generation AI uses the emotion estimation function to analyze the user's emotions and adjust the content and timing of reminders to enhance emotional support. For example, the generation AI sends a study reminder during a time when the user is concentrating. In this way, by adjusting the content and timing of reminders using the emotion estimation function, it is possible to enhance emotional support for the user.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The user information input unit can use voice input or image recognition to enable input in a wider variety of data formats. For example, if a user inputs "I want to be a doctor in the future," the generation AI uses voice recognition technology to convert the input into text data and analyze it. For example, noise cancellation technology can be used to improve the accuracy of voice input. Furthermore, if a user inputs "I want to continue exercising every day" using an image, the generation AI can use image recognition technology to analyze the type and frequency of exercise. For example, it can analyze photos and videos of the exercise and register the details of the exercise in a database. Furthermore, if a user inputs "I never want to stay up late," the generation AI can use voice recognition technology to convert the input into text data and analyze it. For example, it can perform emotion analysis during voice input to evaluate the user's emotional state. This allows for improved user convenience through the use of voice input and image recognition.

[0070] The analysis unit can perform multilingual analysis to accommodate users from different cultures and regions. For example, if a user inputs "I want to be a doctor in the future" in Japanese, the generation AI performs multilingual analysis and translates the input into other languages ​​such as English or Chinese for analysis. For example, machine translation technology is used to improve translation accuracy. Similarly, if a user inputs "I want to continue exercising every day" in Spanish, the generation AI performs multilingual analysis to analyze the type and frequency of exercise. For example, details of the exercise are registered in a database in multiple languages. Similarly, if a user inputs "I never want to stay up late" in French, the generation AI performs multilingual analysis and converts the input into text data for analysis. For example, sentiment analysis is performed in multiple languages ​​to evaluate the user's emotional state. This multilingual analysis allows the system to accommodate users from different cultures and regions.

[0071] The priority setting unit can dynamically reset priorities taking into account changes in the user's life events or environment. For example, if a user inputs "I want to be a doctor in the future" and then inputs "I passed the university entrance exam" as a life event, the generation AI dynamically resets priorities. For example, it sets "Pass the national medical examination" as the next goal. Furthermore, if a user inputs "I want to continue exercising every day" and then inputs "I moved" as an environmental change, the generation AI dynamically resets priorities. For example, it proposes an exercise plan to adapt to the new environment. Furthermore, if a user inputs "I absolutely do not want to stay up late" and then inputs "I started a new job" as a life event, the generation AI dynamically resets priorities. For example, it proposes a sleep plan to adapt to the new job. This allows the generation AI to support the user in achieving their goals by dynamically resetting priorities in response to changes in life events and environment.

[0072] The reminder unit monitors the user's schedule or progress in real time and can provide assistance at the optimal time. For example, when a user inputs, "I want to continue exercising every day," the generation AI monitors the user's schedule and progress in real time and provides exercise assistance at the optimal time. For example, it may remind the user to exercise. Similarly, when a user inputs, "I absolutely do not want to stay up late," the reminder unit monitors the user's schedule and progress in real time and provides sleep assistance at the optimal time. For example, it may send a bedtime reminder. Similarly, when a user inputs, "I want to be a doctor in the future," the reminder unit monitors the user's schedule and progress in real time and provides study assistance at the optimal time. For example, it may remind the user to study. This allows the system to support the user in achieving their goals by monitoring the schedule and progress in real time and providing assistance at the optimal time.

[0073] The reminder unit can link with different devices to realize multi-device reminders. For example, when a user inputs "I want to continue exercising every day," the generation AI links with a smartwatch to send an exercise reminder. For example, the smartwatch will vibrate to notify when it's time to exercise. Also, when a user inputs "I absolutely don't want to stay up late," the reminder unit links with a smart speaker to send a sleep reminder. For example, the smart speaker will play relaxing music before bedtime. Also, when a user inputs "I want to be a doctor in the future," the generation AI links with a smartphone to send a study reminder. For example, the smartphone will display a notification when it's time to study. This allows for linking with different devices to improve user convenience.

[0074] The analysis unit can analyze a user's past behavioral history or achievement level based on user information and reflect this in future goal setting. For example, if a user previously inputs a goal such as "I want to continue exercising every day," the generation AI analyzes the user's past exercise history and evaluates the level of achievement. For example, if the user exercises frequently, the generation AI can prioritize exercise in future goal setting. Similarly, if a user inputs "I want to master English by next year," the analysis unit analyzes the user's past learning history and evaluates the level of achievement. For example, if the user has little time to study, the generation AI can prioritize learning in future goal setting. Similarly, if a user inputs "I absolutely do not want to stay up late," the generation AI can analyze the user's past sleep history and evaluate the level of achievement. For example, if the user frequently stays up late, the generation AI can prioritize sleep in future goal setting. This allows for more realistic goal setting by taking into account past behavioral history and achievement level.

[0075] The analysis unit performs emotional analysis on user information and can adjust the priority of goals based on the intensity or type of emotion. For example, if a user inputs, "I want to be a doctor in the future," the generation AI performs emotional analysis and quantifies the user's passion and motivation. For example, if the emotional score is high, the priority of that goal is set high. Similarly, if a user inputs, "I want to continue exercising every day," the analysis unit performs emotional analysis to evaluate the strength of the user's motivation. For example, if the emotional score is low, the generation AI provides feedback to increase motivation. Similarly, if a user inputs, "I absolutely do not want to stay up late," the analysis unit performs emotional analysis to evaluate the user's stress and anxiety. For example, if the emotional score is high, the generation AI sets the priority of that goal high and provides appropriate assistance. This allows for more effective support by adjusting goal priorities based on the user's emotions.

[0076] The analysis unit can infer emotions when analyzing user information and provide feedback to elicit positive emotions. For example, when a user inputs, "I want to be a doctor in the future," the generation AI can infer emotions and provide feedback to elicit positive emotions. For example, it can display a message such as, "We support your dream!". Also, when a user inputs, "I want to continue exercising every day," the analysis unit can infer emotions and provide feedback to elicit positive emotions. For example, it can display a message such as, "That's a great goal! Let's work hard together!". Also, when a user inputs, "I absolutely do not want to stay up late," the analysis unit can infer emotions and provide feedback to elicit positive emotions. For example, it can display a message such as, "Let's work hard to live a healthy life!". This can elicit positive emotions and increase the user's motivation.

[0077] The priority setting unit uses the emotion estimation function to set priorities based on the user's emotions, allowing them to maintain emotional balance. For example, when a user inputs, "I want to be a doctor in the future," the generation AI uses the emotion estimation function to analyze the user's emotions and set priorities for maintaining emotional balance. For example, it may suggest relaxation time to reduce stress. Furthermore, when a user inputs, "I want to continue exercising every day," the priority setting unit uses the emotion estimation function to analyze the user's emotions and set priorities for maintaining emotional balance. For example, it may suggest a time to refresh after exercise. Furthermore, when a user inputs, "I absolutely do not want to stay up late," the priority setting unit uses the emotion estimation function to analyze the user's emotions and set priorities for maintaining emotional balance. For example, it may suggest a relaxing evening routine. This allows the user to reduce stress and achieve their goals by setting priorities for maintaining emotional balance.

[0078] The reminder unit can use the emotion estimation function to send reminders to enhance emotional motivation. For example, when a user inputs "I want to continue exercising every day," the generation AI estimates the emotion and sends a reminder to enhance emotional motivation. For example, it sends a message such as "Let's do our best today!". Also, when a user inputs "I absolutely do not want to stay up late," the reminder unit estimates the emotion and sends a reminder to enhance emotional motivation. For example, it sends a message such as "Let's do our best to aim for a healthy lifestyle!". Also, when a user inputs "I want to be a doctor in the future," the generation AI estimates the emotion and sends a reminder to enhance emotional motivation. For example, it sends a message such as "We support your dream!". In this way, by sending reminders to enhance emotional motivation, it is possible to support the user in achieving their goals.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The user information input section inputs information about the user's dreams, hopes, future goals, and future goals. For example, a user can input information such as "I want to be a doctor in the future," "I want to master English by next year," "I want to continue exercising every day," and "I absolutely do not want to stay up late at night." Step 2: The analysis unit analyzes the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to understand the user's goals and wishes. The generation AI can also perform sentiment analysis to analyze the user's emotions. Step 3: The priority setting unit sets priorities based on the user information analyzed by the analysis unit. For example, the generation AI suggests the optimal order for achieving the user's goals. Step 4: The reminder unit sends reminders based on the priorities set by the priority setting unit. For example, the generation AI takes into account the user's schedule and progress and sends reminders at the optimal time.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 user information input section for inputting information about the user's dreams, hopes, things to do in the future, and things that the user does not want to do in the future; an analysis unit that analyzes the user information input by the user information input unit; a priority setting unit that sets priorities based on the user information analyzed by the analysis unit; a reminder unit that transmits a reminder based on the priority set by the priority setting unit. A system characterized by:

2. The analysis unit Performing a sentiment analysis on the user information and adjusting the priority of goals based on the intensity or type of sentiment The system of claim 1 .

3. The analysis unit Based on the user information, the user's past behavior history or achievement level is analyzed and reflected in the future goal setting. The system of claim 1 .

4. The analysis unit Inferring emotions when analyzing the user information and providing feedback to elicit positive emotions The system of claim 1 .

5. The user information input unit Allows input in a wider variety of data formats using voice input or image recognition The system of claim 1 .

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