System

Abstract

An object of the system according to the embodiment is to provide support for accumulating daily small successful experiences to a person who has difficulty in taking a step to a new challenge or a social scene.SOLUTION: A system according to an embodiment includes an activity recording unit, a goal suggesting unit, a feedback providing unit, a communication hint providing unit, a relaxation method providing unit, and an advice providing unit. The activity recording unit records daily activities and challenges of the user. The goal suggestion unit suggests a small goal that can be achieved based on the information recorded by the activity recording unit. The feedback providing unit provides positive feedback when the goal proposed by the goal proposing unit is achieved. The communication hint providing unit provides a hint of communication in a social situation. The relaxation method providing unit provides a breathing method for relaxation. The advice providing unit provides practical advice for overcoming a stranger.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 technology has the problem that it does not provide sufficient support to help people who find it difficult to take on new challenges or take the first step into social situations accumulate small, everyday successes.

[0005] The system according to the embodiment aims to provide support to people who find it difficult to take the first step into new challenges or social situations, by helping them accumulate small, everyday successes. [Means for solving the problem]

[0006] The system according to the embodiment includes an activity recording unit, a goal suggestion unit, a feedback providing unit, a communication hint providing unit, a relaxation technique providing unit, and an advice providing unit. The activity recording unit records the user's daily activities and challenges. The goal suggestion unit suggests achievable small goals based on the information recorded by the activity recording unit. The feedback providing unit provides positive feedback when a goal suggested by the goal suggestion unit is achieved. The communication hint providing unit provides communication hints for social situations. The relaxation technique providing unit suggests breathing techniques for relaxation. The advice providing unit provides practical advice for overcoming shyness. [Effects of the Invention]

[0007] The system according to the embodiment can provide support to people who find it difficult to take the first step into new challenges or social situations, helping them accumulate small, everyday successes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Encourage Companion AI system according to an embodiment of the present invention records the user's daily activities and challenges, and the generative AI suggests small achievable goals, provides positive feedback when the goals are achieved, offers tips on communication in social situations, breathing exercises for relaxation, and practical advice for overcoming shyness. In this way, the Encourage Companion AI system allows the user to accumulate small daily successes and build self-confidence.

[0029] The encouragement companion AI system according to the embodiment includes an activity recording unit, a goal suggestion unit, a feedback providing unit, a communication hint providing unit, a relaxation technique providing unit, and an advice providing unit. The activity recording unit records the user's daily activities and challenges. For example, when a user inputs, "I tried a new recipe today," the generation AI records that information and uses it to set goals for the next time. The activity recording unit records the user's activities in detail and converts them into a format that the generation AI can easily analyze. The goal suggestion unit suggests small, achievable goals based on the user's past activities and challenges. For example, it suggests a specific goal such as, "Try taking a 10-minute walk tomorrow." The generation AI generates appropriate goals according to the user's situation. The feedback providing unit provides positive feedback when the user achieves a goal. For example, it praises the user by saying, "Great! You achieved a 10-minute walk today." The communication hint providing unit provides communication hints for social situations. For example, the system provides specific advice such as, "When talking to someone you meet for the first time, introduce yourself first." The relaxation technique providing unit provides breathing techniques to help the user relax. For example, the system provides specific instructions such as, "Take three deep breaths." The advice providing unit provides practical advice to help the user overcome shyness. For example, the system provides specific advice such as, "The next time you're in a social situation, make sure to listen carefully to what the other person is saying." This allows the encouragement companion AI system according to the embodiment to accumulate small daily successes and build self-confidence. For example, the system may develop a positive attitude toward new challenges and become more confident in social situations. Furthermore, by utilizing breathing techniques and communication tips for relaxation, users can reduce stress and build better relationships.

[0030] The activity recording unit can add voice input to the activity record, allowing the user to report their activities by voice. The activity recording unit adds a function that allows the user to report their daily activities by voice, for example. For example, when the user reports by voice, "I tried a new recipe today," the content is automatically converted into text and recorded. This allows the user to report their activities by voice, thereby reducing the effort required for recording activities.

[0031] In the activity recording section, the generation AI automatically suggests related activities, broadening the user's options. For example, when a user records their daily activities, the generation AI automatically suggests related activities. For example, if a user enters, "I tried a new recipe today," the generation AI might suggest, "Next, try making dessert." This allows the generation AI to automatically suggest related activities, broadening the user's options.

[0032] The activity recording unit can link the activity records with other health management apps to generate comprehensive health data. For example, the activity recording unit links the user's daily activity records with other health management apps to generate comprehensive health data. For example, if the user inputs "I tried a new recipe today," that information is also reflected in the health management app. In this way, by linking the activity records with other health management apps, comprehensive health data can be generated.

[0033] The activity recording unit can provide a dashboard that visually displays the user's activity record, allowing the user to check progress at a glance. The activity recording unit provides, for example, a dashboard that visually displays the user's activity record. For example, if the user inputs, "I tried a new recipe today," that information is displayed on the dashboard, allowing the user to check progress at a glance. In this way, by visually displaying the user's activity record, the user's progress can be checked at a glance.

[0034] The goal suggestion unit allows the generation AI to analyze the user's past successful experiences and identify and suggest the most effective goal setting pattern. For example, if the user inputs, "Today I tried a new recipe," the generation AI will suggest, "Next, try making dessert," based on the user's past successful experiences. In this way, the generation AI analyzes the user's past successful experiences and identifies and suggests the most effective goal setting pattern, thereby improving the user's goal achievement rate.

[0035] The goal suggestion unit learns the user's lifestyle and habits and can suggest goals at the optimal time. For example, if a user inputs, "I tried a new recipe today," the generation AI will take the user's lifestyle into consideration and suggest, "Next, try making a dessert this weekend." This allows the AI ​​to learn the user's lifestyle and habits and suggest goals at the optimal time, improving the user's goal achievement rate.

[0036] The goal suggestion unit can share the goal suggestion with other users and promote mutual support within the community. For example, the goal suggestion unit shares the goal suggested by the generation AI with other users and promotes mutual support within the community. For example, if a user inputs "I tried a new recipe today," the goal is shared within the community and receives support from other users. In this way, by sharing the goal suggestion with other users, mutual support within the community can be promoted.

[0037] The goal suggestion unit can provide options that allow the user to customize the goals suggested by the generation AI. For example, if a user inputs "I tried a new recipe today," the goal can be customized to "Next, let's try making dessert." This improves user satisfaction by providing options that allow the user to customize the goals suggested by the generation AI.

[0038] The feedback providing unit can add audio or visual elements to the feedback to give it a stronger emotional impact. For example, if a user inputs, "I tried a new recipe today," the generation AI provides audio feedback saying, "Great!" By adding audio or visual elements to the feedback, it is possible to give it a stronger emotional impact.

[0039] The feedback providing unit can share the feedback with other users and promote mutual evaluation within the community. For example, the feedback providing unit shares the feedback provided by the generation AI with other users and promotes mutual evaluation within the community. For example, when a user inputs "I tried a new recipe today," the feedback is shared within the community and receives evaluation from other users. In this way, by sharing the feedback with other users, mutual evaluation within the community can be promoted.

[0040] The feedback providing unit can add a function that allows the user to respond to the feedback provided by the generation AI. The feedback providing unit adds a function that allows the user to respond to the feedback provided by the generation AI. For example, if the user inputs "I tried a new recipe today," the user can respond to that feedback by saying "Thanks!" In this way, by adding a function that allows the user to respond to the feedback provided by the generation AI, the effectiveness of the feedback can be increased.

[0041] The communication hint providing unit allows the generation AI to analyze the user's past communication history and identify and provide the most effective hint. For example, if a user inputs, "I tried a new recipe today," the generation AI provides a hint, "Next time, try telling your friends about that recipe," based on the past communication history. In this way, the generation AI can analyze the user's past communication history, identify, and provide the most effective hint, thereby improving the user's communication skills.

[0042] The communication hint providing unit can learn the user's communication style and provide individually customized hints. For example, the generation AI of the communication hint providing unit learns the user's communication style and provides individually customized hints. For example, when a user inputs, "I tried a new recipe today," the generation AI takes the user's style into consideration and provides a hint such as, "Next time, try telling your friends about that recipe." In this way, by learning the user's communication style and providing individually customized hints, the user's communication skills can be improved.

[0043] The communication hint providing unit can share the communication hint with other users and promote mutual support within the community. The communication hint providing unit, for example, shares the communication hint provided by the generation AI with other users and promotes mutual support within the community. For example, if a user inputs "I tried a new recipe today," the hint is shared within the community and the user receives support from other users. In this way, by sharing the communication hint with other users, mutual support within the community can be promoted.

[0044] The communication hint providing unit can add a function that allows the user to give feedback to the hints provided by the generation AI. The communication hint providing unit adds a function that allows the user to give feedback to the communication hints provided by the generation AI, for example. For example, if the user inputs "I tried a new recipe today," the user can give feedback in response to the hint, saying "Thanks!" In this way, by adding a function that allows the user to give feedback to the hints provided by the generation AI, the effectiveness of the hints can be improved.

[0045] The relaxation method provision unit allows the generation AI to monitor the user's stress level in real time and suggest breathing methods at the optimal timing. For example, if a user inputs, "I tried a new recipe today," the generation AI will monitor the user's stress level and suggest, "Take three deep breaths" at the appropriate time. In this way, the generation AI can monitor the user's stress level in real time and suggest breathing methods at the optimal timing, effectively reducing the user's stress.

[0046] The relaxation method providing unit can add audio guidance to suggested breathing methods to help the user feel more relaxed. For example, if a user inputs, "I tried a new recipe today," the generation AI provides audio guidance such as, "Take three deep breaths." By adding audio guidance to suggested breathing methods, the user can feel more relaxed.

[0047] The relaxation method providing unit can provide suggestions for breathing methods in combination with other relaxation techniques (e.g., meditation or yoga). For example, if a user inputs, "I tried a new recipe today," the generation AI suggests, "Take three deep breaths, then meditate for five minutes." This allows the user to enhance their relaxation by providing suggestions for breathing methods in combination with other relaxation techniques.

[0048] The advice providing unit allows the generation AI to analyze the user's past advice history and identify and provide the most effective advice. For example, if a user inputs, "I tried a new recipe today," the generation AI will advise, "Next time, try telling your friends about that recipe," based on the past advice history. In this way, the generation AI can analyze the user's past advice history and identify and provide the most effective advice, thereby promoting improvements in user behavior.

[0049] The advice providing unit can monitor the user's progress in real time and provide advice at the appropriate time. For example, if the generation AI monitors the user's progress in real time and provides advice at the appropriate time, when the user inputs, "Today I tried a new recipe," the AI ​​monitors the user's progress and advises, at the appropriate time, "Next time, try telling your friends about that recipe." In this way, by monitoring the user's progress in real time and providing advice at the appropriate time, it is possible to promote improvements in the user's behavior.

[0050] The advice providing unit can share the advice with other users and promote mutual support within the community. The advice providing unit, for example, shares the advice provided by the generation AI with other users and promotes mutual support within the community. For example, if a user inputs "I tried a new recipe today," the advice is shared within the community and the user receives support from other users. In this way, by sharing advice with other users, mutual support within the community can be promoted.

[0051] The advice providing unit can add a function that allows the user to give feedback to the advice provided by the generation AI. For example, the advice providing unit adds a function that allows the user to give feedback to the advice provided by the generation AI. For example, if the user inputs "I tried a new recipe today," the user can return feedback such as "Thanks!" in response to that advice. In this way, by adding a function that allows the user to give feedback to the advice provided by the generation AI, the effectiveness of the advice can be improved.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The Encourage Companion AI system can also identify a user's hobbies and interests based on their activity records and suggest new activities related to them. For example, if a user inputs, "I tried a new recipe today," the Generative AI can suggest, "Next, try taking part in a cooking class." Or, if a user inputs, "I went jogging today," it can suggest, "Next, try participating in a marathon." This can improve the user's quality of life by suggesting new activities based on their hobbies and interests.

[0054] The Encourage Companion AI system can also monitor the user's health status and provide health advice based on the user's activity record. For example, if a user inputs, "I tried a new recipe today," the generating AI will advise, "Next time, try to eat a balanced meal." Similarly, if a user inputs, "I went jogging today," it can advise, "Next, try stretching to loosen up your body." In this way, the system can monitor the user's health status and provide appropriate advice to support their health.

[0055] The Encourage Companion AI system can also monitor a user's learning progress and provide learning advice based on their activity records. For example, if a user inputs, "Today I tried a new recipe," the generating AI can advise, "Next, learn the basics of cooking." Or, if a user inputs, "Today I studied English," it can advise, "Next, try practicing English conversation." This allows the system to monitor a user's learning progress and provide appropriate advice, thereby improving the user's learning effectiveness.

[0056] The Encourage Companion AI system can also provide event information related to the user's hobbies and interests based on the user's activity records. For example, if a user inputs, "I tried a new recipe today," the generation AI can provide event information such as, "Next, try participating in a cooking festival." Or, if a user inputs, "I went jogging today," it can provide event information such as, "Next, try participating in a marathon." This can improve the user's quality of life by providing event information related to the user's hobbies and interests.

[0057] The Encourage Companion AI system can also suggest online courses that will help improve the user's skills based on the user's activity records. For example, if a user inputs, "Today I tried a new recipe," the generation AI will suggest, "Next, try taking an online cooking course." Or, if a user inputs, "Today I studied English," it can suggest, "Next, try taking an online English conversation course." This can improve the user's learning effectiveness by suggesting online courses that will help improve the user's skills.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The activity recorder records the user's daily activities and challenges. For example, if the user inputs, "I tried a new recipe today," the generation AI records that information and uses it to set goals for the next time. The activity recorder also records the user's activities in detail and converts them into a format that is easy for the generation AI to analyze. Step 2: In the goal suggestion section, the generation AI suggests small, achievable goals based on the user's past activities and challenge records. For example, it suggests a specific goal such as "Try taking a 10-minute walk tomorrow." The generation AI generates appropriate goals based on the user's situation. Step 3: In the feedback section, the AI ​​generator provides positive feedback when the user achieves their goal. For example, it praises the user by saying, "Great! You achieved a 10-minute walk today." Step 4: The communication hint provider provides communication hints for social situations, such as "When talking to someone you meet for the first time, introduce yourself first." Step 5: The relaxation technique providing unit provides breathing techniques to help the user relax. For example, it provides specific instructions such as "Take three deep breaths." Step 6: The advice section provides practical advice on overcoming shyness, such as, "Next time you're in a social situation, try to be more conscious of listening to what the other person is saying."

[0060] (Example 2) The Encourage Companion AI system according to an embodiment of the present invention records the user's daily activities and challenges, and the generative AI suggests small achievable goals, provides positive feedback when the goals are achieved, offers tips on communication in social situations, breathing exercises for relaxation, and practical advice for overcoming shyness. In this way, the Encourage Companion AI system allows the user to accumulate small daily successes and build self-confidence.

[0061] The encouragement companion AI system according to the embodiment includes an activity recording unit, a goal suggestion unit, a feedback providing unit, a communication hint providing unit, a relaxation technique providing unit, and an advice providing unit. The activity recording unit records the user's daily activities and challenges. For example, when a user inputs, "I tried a new recipe today," the generation AI records that information and uses it to set goals for the next time. The activity recording unit records the user's activities in detail and converts them into a format that the generation AI can easily analyze. The goal suggestion unit suggests small, achievable goals based on the user's past activities and challenges. For example, it suggests a specific goal such as, "Try taking a 10-minute walk tomorrow." The generation AI generates appropriate goals according to the user's situation. The feedback providing unit provides positive feedback when the user achieves a goal. For example, it praises the user by saying, "Great! You achieved a 10-minute walk today." The communication hint providing unit provides communication hints for social situations. For example, the system provides specific advice such as, "When talking to someone you meet for the first time, introduce yourself first." The relaxation technique providing unit provides breathing techniques to help the user relax. For example, the system provides specific instructions such as, "Take three deep breaths." The advice providing unit provides practical advice to help the user overcome shyness. For example, the system provides specific advice such as, "The next time you're in a social situation, make sure to listen carefully to what the other person is saying." This allows the encouragement companion AI system according to the embodiment to accumulate small daily successes and build self-confidence. For example, the system may develop a positive attitude toward new challenges and become more confident in social situations. Furthermore, by utilizing breathing techniques and communication tips for relaxation, users can reduce stress and build better relationships.

[0062] The activity recording unit can simultaneously record the user's emotional state using the emotion estimation function and track changes in emotions. For example, when a user records their daily activities, the generation AI simultaneously records the user's emotional state using the emotion estimation function. For example, if a user enters "I tried a new recipe today," the emotional state at that time (joy, excitement, etc.) is also recorded. This allows the user's emotional state to be simultaneously recorded and changes in emotion to be tracked, allowing the user's emotional patterns to be analyzed.

[0063] The activity recording unit can add voice input to the activity record, allowing the user to report their activities by voice. The activity recording unit adds a function that allows the user to report their daily activities by voice, for example. For example, when the user reports by voice, "I tried a new recipe today," the content is automatically converted into text and recorded. This allows the user to report their activities by voice, thereby reducing the effort required for recording activities.

[0064] In the activity recording section, the generation AI automatically suggests related activities, broadening the user's options. For example, when a user records their daily activities, the generation AI automatically suggests related activities. For example, if a user enters, "I tried a new recipe today," the generation AI might suggest, "Next, try making dessert." This allows the generation AI to automatically suggest related activities, broadening the user's options.

[0065] The activity recording unit can link the activity records with other health management apps to generate comprehensive health data. For example, the activity recording unit links the user's daily activity records with other health management apps to generate comprehensive health data. For example, if the user inputs "I tried a new recipe today," that information is also reflected in the health management app. In this way, by linking the activity records with other health management apps, comprehensive health data can be generated.

[0066] The activity recording unit can provide a dashboard that visually displays the user's activity record, allowing the user to check progress at a glance. The activity recording unit provides, for example, a dashboard that visually displays the user's activity record. For example, if the user inputs, "I tried a new recipe today," that information is displayed on the dashboard, allowing the user to check progress at a glance. In this way, by visually displaying the user's activity record, the user's progress can be checked at a glance.

[0067] The activity recording unit can use the emotion estimation function to analyze the emotions of the user when recording their activities in real time and make suggestions that will elicit positive emotions. The activity recording unit, for example, uses the emotion estimation function to analyze the emotions of the user when recording their activities in real time. For example, if the user inputs, "I tried a new recipe today," the emotional state at that time is analyzed and suggestions that will elicit positive emotions are made. In this way, the emotion estimation function can be used to analyze the user's emotions in real time and make suggestions that will elicit positive emotions, thereby maintaining the user's motivation.

[0068] The goal suggestion unit allows the generation AI to analyze the user's past successful experiences and identify and suggest the most effective goal setting pattern. For example, if the user inputs, "Today I tried a new recipe," the generation AI will suggest, "Next, try making dessert," based on the user's past successful experiences. In this way, the generation AI analyzes the user's past successful experiences and identifies and suggests the most effective goal setting pattern, thereby improving the user's goal achievement rate.

[0069] The goal suggestion unit learns the user's lifestyle and habits and can suggest goals at the optimal time. For example, if a user inputs, "I tried a new recipe today," the generation AI will take the user's lifestyle into consideration and suggest, "Next, try making a dessert this weekend." This allows the AI ​​to learn the user's lifestyle and habits and suggest goals at the optimal time, improving the user's goal achievement rate.

[0070] The goal suggestion unit uses the emotion estimation function to suggest goals according to the user's emotional state, thereby maintaining motivation. The goal suggestion unit, for example, uses the emotion estimation function to suggest goals according to the user's emotional state. For example, if the user inputs, "I tried a new recipe today," the emotional state at that time is analyzed and a goal that elicits positive emotions is suggested. In this way, the emotion estimation function can be used to suggest goals according to the user's emotional state, thereby maintaining the user's motivation.

[0071] The goal suggestion unit can share the goal suggestion with other users and promote mutual support within the community. For example, the goal suggestion unit shares the goal suggested by the generation AI with other users and promotes mutual support within the community. For example, if a user inputs "I tried a new recipe today," the goal is shared within the community and receives support from other users. In this way, by sharing the goal suggestion with other users, mutual support within the community can be promoted.

[0072] The goal suggestion unit can provide options that allow the user to customize the goals suggested by the generation AI. For example, if a user inputs "I tried a new recipe today," the goal can be customized to "Next, let's try making dessert." This improves user satisfaction by providing options that allow the user to customize the goals suggested by the generation AI.

[0073] The goal suggestion unit can use the emotion estimation function to analyze the emotion the user feels when they achieve their goal and reflect it in the next goal setting. The goal suggestion unit, for example, uses the emotion estimation function to analyze the emotion the user feels when they achieve their goal. For example, if the user inputs, "I tried a new recipe today," the emotional state at that time is analyzed and reflected in the next goal setting. In this way, by using the emotion estimation function to analyze the emotion the user feels when they achieve their goal and reflecting it in the next goal setting, the user's motivation can be maintained.

[0074] The feedback providing unit can add audio or visual elements to the feedback to give it a stronger emotional impact. For example, if a user inputs, "I tried a new recipe today," the generation AI provides audio feedback saying, "Great!" By adding audio or visual elements to the feedback, it is possible to give it a stronger emotional impact.

[0075] The feedback providing unit can use the emotion estimation function to provide feedback according to the user's emotional state and reinforce positive emotions. The feedback providing unit, for example, uses the emotion estimation function to provide feedback according to the user's emotional state. For example, if the user inputs, "I tried a new recipe today," the emotional state at that time is analyzed and feedback that reinforces positive emotions is provided. In this way, by providing feedback according to the user's emotional state using the emotion estimation function, positive emotions can be reinforced.

[0076] The feedback providing unit can share the feedback with other users and promote mutual evaluation within the community. For example, the feedback providing unit shares the feedback provided by the generation AI with other users and promotes mutual evaluation within the community. For example, when a user inputs "I tried a new recipe today," the feedback is shared within the community and receives evaluation from other users. In this way, by sharing the feedback with other users, mutual evaluation within the community can be promoted.

[0077] The feedback providing unit can add a function that allows the user to respond to the feedback provided by the generation AI. The feedback providing unit adds a function that allows the user to respond to the feedback provided by the generation AI. For example, if the user inputs "I tried a new recipe today," the user can respond to that feedback by saying "Thanks!" In this way, by adding a function that allows the user to respond to the feedback provided by the generation AI, the effectiveness of the feedback can be increased.

[0078] The feedback providing unit can use the emotion estimation function to analyze the emotion of the user when receiving feedback in real time and reflect it in the next feedback. The feedback providing unit, for example, uses the emotion estimation function to analyze the emotion of the user when receiving feedback in real time. For example, if the user inputs, "I tried a new recipe today," the emotional state when receiving that feedback is analyzed and reflected in the next feedback. In this way, the emotion estimation function can be used to analyze the emotion of the user when receiving feedback in real time and reflect it in the next feedback, thereby increasing the effectiveness of the feedback.

[0079] The communication hint providing unit allows the generation AI to analyze the user's past communication history and identify and provide the most effective hint. For example, if a user inputs, "I tried a new recipe today," the generation AI provides a hint, "Next time, try telling your friends about that recipe," based on the past communication history. In this way, the generation AI can analyze the user's past communication history, identify, and provide the most effective hint, thereby improving the user's communication skills.

[0080] The communication hint providing unit can learn the user's communication style and provide individually customized hints. For example, the generation AI of the communication hint providing unit learns the user's communication style and provides individually customized hints. For example, when a user inputs, "I tried a new recipe today," the generation AI takes the user's style into consideration and provides a hint such as, "Next time, try telling your friends about that recipe." In this way, by learning the user's communication style and providing individually customized hints, the user's communication skills can be improved.

[0081] The communication hint providing unit uses the emotion estimation function to provide communication hints according to the emotional state of the user, thereby reducing tension. The communication hint providing unit uses, for example, the emotion estimation function to provide communication hints according to the emotional state of the user. For example, when a user inputs, "I tried a new recipe today," the emotional state at that time is analyzed and a hint for reducing tension is provided. In this way, by using the emotion estimation function to provide communication hints according to the emotional state of the user, tension can be reduced.

[0082] The communication hint providing unit can share the communication hint with other users and promote mutual support within the community. The communication hint providing unit, for example, shares the communication hint provided by the generation AI with other users and promotes mutual support within the community. For example, if a user inputs "I tried a new recipe today," the hint is shared within the community and the user receives support from other users. In this way, by sharing the communication hint with other users, mutual support within the community can be promoted.

[0083] The communication hint providing unit can add a function that allows the user to give feedback to the hints provided by the generation AI. The communication hint providing unit adds a function that allows the user to give feedback to the communication hints provided by the generation AI, for example. For example, if the user inputs "I tried a new recipe today," the user can give feedback in response to the hint, saying "Thanks!" In this way, by adding a function that allows the user to give feedback to the hints provided by the generation AI, the effectiveness of the hints can be improved.

[0084] The communication hint providing unit can use the emotion estimation function to analyze the emotion of the user when he / she puts a hint into practice in real time and reflect it in the next hint. The communication hint providing unit, for example, uses the emotion estimation function to analyze the emotion of the user when he / she puts a hint into practice in real time. For example, if the user inputs "I tried a new recipe today," the emotional state when he / she puts the hint into practice is analyzed and reflected in the next hint. In this way, the emotion estimation function can be used to analyze the emotion of the user when he / she puts a hint into practice in real time and reflect it in the next hint, thereby increasing the effectiveness of the hint.

[0085] The relaxation method provision unit allows the generation AI to monitor the user's stress level in real time and suggest breathing methods at the optimal timing. For example, if a user inputs, "I tried a new recipe today," the generation AI will monitor the user's stress level and suggest, "Take three deep breaths" at the appropriate time. In this way, the generation AI can monitor the user's stress level in real time and suggest breathing methods at the optimal timing, effectively reducing the user's stress.

[0086] The relaxation method providing unit can add audio guidance to suggested breathing methods to help the user feel more relaxed. For example, if a user inputs, "I tried a new recipe today," the generation AI provides audio guidance such as, "Take three deep breaths." By adding audio guidance to suggested breathing methods, the user can feel more relaxed.

[0087] The relaxation method providing unit uses the emotion estimation function to provide a relaxation method that corresponds to the user's emotional state, thereby reducing stress. The relaxation method providing unit uses, for example, the emotion estimation function to provide a relaxation method that corresponds to the user's emotional state. For example, if a user inputs, "I tried a new recipe today," the unit analyzes the user's emotional state and suggests a relaxation method to reduce stress. In this way, by using the emotion estimation function to provide a relaxation method that corresponds to the user's emotional state, the user's stress can be reduced.

[0088] The relaxation method providing unit can provide suggestions for breathing methods in combination with other relaxation techniques (e.g., meditation or yoga). For example, if a user inputs, "I tried a new recipe today," the generation AI suggests, "Take three deep breaths, then meditate for five minutes." This allows the user to enhance their relaxation by providing suggestions for breathing methods in combination with other relaxation techniques.

[0089] The relaxation technique provision unit can use the emotion estimation function to analyze in real time the emotions felt when the user practices a breathing technique and reflect the emotions in the next suggestion. The relaxation technique provision unit, for example, uses the emotion estimation function to analyze in real time the emotions felt when the user practices a breathing technique. For example, if the user inputs, "I tried a new recipe today," the emotional state when the user practiced that breathing technique is analyzed and reflected in the next suggestion. In this way, the emotion estimation function can be used to analyze in real time the emotions felt when the user practices a breathing technique and reflect the emotions in the next suggestion, thereby enhancing the effectiveness of the relaxation technique.

[0090] The advice providing unit allows the generation AI to analyze the user's past advice history and identify and provide the most effective advice. For example, if a user inputs, "I tried a new recipe today," the generation AI will advise, "Next time, try telling your friends about that recipe," based on the past advice history. In this way, the generation AI can analyze the user's past advice history and identify and provide the most effective advice, thereby promoting improvements in user behavior.

[0091] The advice providing unit can monitor the user's progress in real time and provide advice at the appropriate time. For example, if the generation AI monitors the user's progress in real time and provides advice at the appropriate time, when the user inputs, "Today I tried a new recipe," the AI ​​monitors the user's progress and advises, at the appropriate time, "Next time, try telling your friends about that recipe." In this way, by monitoring the user's progress in real time and providing advice at the appropriate time, it is possible to promote improvements in the user's behavior.

[0092] The advice providing unit uses the emotion estimation function to provide advice according to the emotional state of the user, thereby maintaining motivation. The advice providing unit uses, for example, the emotion estimation function to provide advice according to the emotional state of the user. For example, if the user inputs, "I tried a new recipe today," the advice providing unit analyzes the emotional state and provides advice to maintain motivation. In this way, by using the emotion estimation function to provide advice according to the emotional state of the user, the user's motivation can be maintained.

[0093] The advice providing unit can share the advice with other users and promote mutual support within the community. The advice providing unit, for example, shares the advice provided by the generation AI with other users and promotes mutual support within the community. For example, if a user inputs "I tried a new recipe today," the advice is shared within the community and the user receives support from other users. In this way, by sharing advice with other users, mutual support within the community can be promoted.

[0094] The advice providing unit can add a function that allows the user to give feedback to the advice provided by the generation AI. For example, the advice providing unit adds a function that allows the user to give feedback to the advice provided by the generation AI. For example, if the user inputs "I tried a new recipe today," the user can return feedback such as "Thanks!" in response to that advice. In this way, by adding a function that allows the user to give feedback to the advice provided by the generation AI, the effectiveness of the advice can be improved.

[0095] The advice providing unit can use the emotion estimation function to analyze in real time the emotions felt when the user puts the advice into practice and reflect the emotions in the next advice. The advice providing unit, for example, uses the emotion estimation function to analyze in real time the emotions felt when the user puts the advice into practice. For example, if the user inputs, "I tried a new recipe today," the emotional state when the advice was put into practice is analyzed and reflected in the next advice. In this way, the effectiveness of the advice can be improved by using the emotion estimation function to analyze in real time the emotions felt when the user puts the advice into practice and reflecting the emotions in the next advice.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The Encourage Companion AI system can also identify a user's hobbies and interests based on their activity records and suggest new activities related to them. For example, if a user inputs, "I tried a new recipe today," the Generative AI can suggest, "Next, try taking part in a cooking class." Or, if a user inputs, "I went jogging today," it can suggest, "Next, try participating in a marathon." This can improve the user's quality of life by suggesting new activities based on their hobbies and interests.

[0098] The Encourage Companion AI system's relaxation technique provider can also suggest relaxation music appropriate for the user based on the user's emotional state. For example, if a user inputs "I tried a new recipe today" and the AI ​​generator estimates that the user's emotional state at that time is stressed, it can suggest "Let's listen to some relaxing music." Similarly, if a user inputs "I'm tired from work today," it can suggest "Let's listen to some relaxing jazz music." This allows the system to reduce the user's stress by suggesting relaxation music that matches the user's emotional state.

[0099] The Encourage Companion AI system can also monitor the user's health status and provide health advice based on the user's activity record. For example, if a user inputs, "I tried a new recipe today," the generating AI will advise, "Next time, try to eat a balanced meal." Similarly, if a user inputs, "I went jogging today," it can advise, "Next, try stretching to loosen up your body." In this way, the system can monitor the user's health status and provide appropriate advice to support their health.

[0100] The Encourage Companion AI system can also suggest communication skills appropriate to the user based on their emotional state through its communication hint provider. For example, if a user inputs, "I tried a new recipe today," and the system estimates that their emotional state at the time is tense, the AI ​​generator can suggest, "Try some tips to help you speak more relaxed." Similarly, if a user inputs, "I had a hard time talking to my friend today," the AI ​​generator can suggest, "Next time, try to be more conscious of listening to what the other person is saying." This allows the AI ​​to suggest communication skills appropriate to the user's emotional state, thereby improving the user's communication abilities.

[0101] The Encourage Companion AI system can also monitor a user's learning progress and provide learning advice based on their activity records. For example, if a user inputs, "Today I tried a new recipe," the generating AI can advise, "Next, learn the basics of cooking." Or, if a user inputs, "Today I studied English," it can advise, "Next, try practicing English conversation." This allows the system to monitor a user's learning progress and provide appropriate advice, thereby improving the user's learning effectiveness.

[0102] The Encourage Companion AI system's feedback provider can also provide appropriate positive feedback to users based on their emotional state. For example, if a user inputs, "I tried a new recipe today," and the system estimates their emotional state at that time to be joyful, the AI ​​generator will provide feedback such as, "Great! Let's do our best next time." Similarly, if a user inputs, "I'm tired from work today," the AI ​​generator can provide feedback such as, "Thank you for your hard work. Please relax and take a rest." This allows the system to maintain the user's motivation by providing positive feedback that reflects their emotional state.

[0103] The Encourage Companion AI system can also provide event information related to the user's hobbies and interests based on the user's activity records. For example, if a user inputs, "I tried a new recipe today," the generation AI can provide event information such as, "Next, try participating in a cooking festival." Or, if a user inputs, "I went jogging today," it can provide event information such as, "Next, try participating in a marathon." This can improve the user's quality of life by providing event information related to the user's hobbies and interests.

[0104] The Encourage Companion AI system's relaxation method provider can also suggest relaxation exercises appropriate for the user based on the user's emotional state. For example, if a user inputs "I tried a new recipe today" and the system estimates that the user's emotional state at that time is stressed, the AI ​​generator can suggest "Try some yoga poses." Similarly, if a user inputs "I'm tired from work today," the AI ​​generator can suggest "Try five minutes of meditation." This allows the system to reduce the user's stress by suggesting relaxation exercises that match the user's emotional state.

[0105] The Encourage Companion AI system can also suggest online courses that will help improve the user's skills based on the user's activity records. For example, if a user inputs, "Today I tried a new recipe," the generation AI will suggest, "Next, try taking an online cooking course." Or, if a user inputs, "Today I studied English," it can suggest, "Next, try taking an online English conversation course." This can improve the user's learning effectiveness by suggesting online courses that will help improve the user's skills.

[0106] The Encourage Companion AI system's advice provider can also provide appropriate advice to users based on their emotional state. For example, if a user inputs, "I tried a new recipe today," and the AI ​​predicts that their emotional state at the time is anxiety, the AI ​​generator can advise, "Next time, try starting with an easy recipe." Similarly, if a user inputs, "I'm tired from work today," the AI ​​generator can advise, "Try to relax and get some rest." This allows the system to provide advice tailored to the user's emotional state, helping to maintain their motivation.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The activity recorder records the user's daily activities and challenges. For example, if the user inputs, "I tried a new recipe today," the generation AI records that information and uses it to set goals for the next time. The activity recorder also records the user's activities in detail and converts them into a format that is easy for the generation AI to analyze. Step 2: In the goal suggestion section, the generation AI suggests small, achievable goals based on the user's past activities and challenge records. For example, it suggests a specific goal such as "Try taking a 10-minute walk tomorrow." The generation AI generates appropriate goals based on the user's situation. Step 3: In the feedback section, the AI ​​generator provides positive feedback when the user achieves their goal. For example, it praises the user by saying, "Great! You achieved a 10-minute walk today." Step 4: The communication hint provider provides communication hints for social situations, such as "When talking to someone you meet for the first time, introduce yourself first." Step 5: The relaxation technique providing unit provides breathing techniques to help the user relax. For example, it provides specific instructions such as "Take three deep breaths." Step 6: The advice section provides practical advice on overcoming shyness, such as, "Next time you're in a social situation, try to be more conscious of listening to what the other person is saying."

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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]

[0176] 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. An activity recorder that records the user's daily activities and challenges; a goal suggestion unit that suggests achievable small goals based on the information recorded by the activity recording unit; a feedback providing unit that provides positive feedback when the goal proposed by the goal proposing unit is achieved; A communication hint section provides communication tips for social situations, a relaxation method providing unit that provides breathing methods for relaxation; An advice providing unit that provides practical advice for overcoming shyness. A system characterized by:

2. The activity recording unit While recording the user's activities, an emotion estimation function is used to simultaneously record the user's emotional state and track changes in emotion.

2. The system of claim 1.

3. The goal suggestion unit The generative AI analyzes the user's past success experiences, identifies the most effective goal setting pattern, and makes the suggestion.

2. The system of claim 1.

4. The feedback providing unit: The generation AI analyzes the user's past feedback history, identifies the most effective feedback pattern, and provides it.

2. The system of claim 1.

5. The communication hint providing unit A generative AI analyzes the user's past communication history, identifies the most effective hints, and provides them.

2. The system of claim 1.

6. The relaxation method providing unit The generating AI monitors the user's stress level in real time and suggests the breathing technique at the optimal timing.

2. The system of claim 1.

7. The advice providing unit The generation AI analyzes the user's past advice history, identifies the most effective advice, and provides it.

2. The system of claim 1.

8. The goal suggestion unit Using an emotion estimation function, the goal is proposed according to the emotional state of the user, and motivation is maintained.

2. The system of claim 1.

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