System

Abstract

An object of a system according to an embodiment is to generate appropriate feedback and suggestions based on input data of a user.SOLUTION: A system according to an embodiment includes a data input unit, an analysis unit, a feedback generation unit, and a proposal generation unit. The data input unit inputs data from a user. The analysis unit analyzes the data input by the data input unit. The feedback generation unit generates feedback based on the data analyzed by the analysis unit. The proposal generation unit generates a proposal based on the feedback generated by the feedback generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately generate appropriate feedback and suggestions based on user input data, and there is room for improvement.

[0005] The system according to the embodiment aims to generate appropriate feedback and suggestions based on user input data. [Means for solving the problem]

[0006] A system according to an embodiment includes a data input unit, an analysis unit, a feedback generation unit, and a suggestion generation unit. The data input unit inputs data from a user. The analysis unit analyzes the data input by the data input unit. The feedback generation unit generates feedback based on the data analyzed by the analysis unit. The suggestion generation unit generates suggestions based on the feedback generated by the feedback generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate appropriate feedback and suggestions based on the user's input data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) In a diary support system according to an embodiment of the present invention, a user communicates diary content, emotions, voice, and image data to an AI model, and the AI ​​model supports the user through appropriate feedback and questions. The user receives answers and suggestions from the AI ​​model and writes the diary content based on these answers and suggestions. This allows the diary support system to receive support from the generation AI when the user writes a diary, thereby enriching the content of the diary. For example, by recording daily events and emotions in detail, the user can deepen self-understanding. Furthermore, the feedback and questions from the generation AI can provide new perspectives and insights.

[0029] A diary support system according to an embodiment includes a data input unit, an analysis unit, a feedback generation unit, and a suggestion generation unit. The data input unit inputs data from a user. For example, the user can input text such as "Today was a really fun day," speak verbally such as "I went to the park with my friends today," or upload images of photos taken in the park. The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes the text data using a text generation AI (e.g., LLM), analyzes the audio data using speech recognition technology, and analyzes the image data using image recognition technology. The feedback generation unit generates feedback based on the data analyzed by the analysis unit. For example, if a user inputs "Today was a really fun day," the generation AI generates feedback such as "What did you enjoy?" The suggestion generation unit generates suggestions based on the feedback generated by the feedback generation unit. For example, if a user answers "I played soccer with my friends," the generation AI generates suggestions such as "Did you enjoy soccer? What other sports do you like?" This allows the diary support system according to an embodiment to analyze user data and provide appropriate feedback and suggestions. For example, when a user writes a diary, they can receive support from the generation AI to enrich the content of the diary.

[0030] The data input unit compares data entered by the user with past input data, detects patterns and trends, and provides feedback. For example, when a user enters, "Today was a really fun day," the generation AI compares it with past input data and detects that similar positive emotions have continued, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, when a user utters, "I went to the park with my friends today," the generation AI compares it with past voice data and detects that you often hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, when a user uploads photos taken at the park as image data, the generation AI compares it with past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided by comparing it with past data.

[0031] The data input unit supports input in different languages, enabling a multilingual diary system to be provided. For example, when a user inputs "Today was a very enjoyable day" in Japanese, the generation AI translates the content into English and displays "I had a very enjoyable day today," thereby providing a multilingual diary system. Also, when a user speaks in Spanish, "I went to the park with my friends today," the generation AI translates the content into French and displays "Je suis alle au park avec des amis aujourd'hui," thereby providing a multilingual diary system. Also, when a user uploads a photo taken in a park as image data and enters a description of it in Chinese, the generation AI translates the content into German and displays "Dieses Foto wurde im Park aufgenommen," thereby providing a multilingual diary system. In this way, a multilingual diary system can be provided.

[0032] The data input unit can automatically tag image and audio data with data entered by the user, improving searchability. For example, when a user enters "Today was a really fun day," the generation AI automatically tags the content with tags such as "fun" and "day," making it easier to search later. Also, when a user says by voice, "Today I went to the park with my friends," the generation AI automatically tags the content with tags such as "friends" and "park," making it easier to search later. Also, when a user uploads a photo taken in a park as image data, the generation AI automatically tags the image with tags such as "park" and "photo," making it easier to search later. This improves the searchability of data.

[0033] When analyzing user input data, the analysis unit considers correlation with past data, enabling it to provide more specific feedback. For example, when a user inputs, "Today was a really fun day," the generation AI references past data and detects a continuation of similar positive emotions, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, when a user utters, "Today I went to the park with my friends," the generation AI references past voice data and detects that you frequently hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, when a user uploads photos taken at a park as image data, the generation AI references past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided while taking correlation with past data into account.

[0034] The analysis unit can provide personalized feedback that takes into account the user's lifestyle, hobbies, and preferences. For example, when a user inputs, "Today was a really fun day," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "What did you enjoy? Is it related to your recent hobby?" When a user utters, "Today I went to the park with my friends," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "Oh, you went to the park with your friends. What did you do? Is it related to your recent hobby?" When a user uploads a photo taken in a park as image data, the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "This photo is really nice. Which park was it taken in? Is it related to your recent hobby?" This makes it possible to provide feedback that takes into account the user's lifestyle, hobbies, and preferences.

[0035] The analysis unit can integrate data from different devices and provide comprehensive feedback. For example, if a user inputs "Today was a really fun day" on their smartphone, the analysis unit simultaneously integrates and analyzes heart rate data from their smartwatch and provides feedback such as "It looks like your heart rate is up. What did you enjoy?" Alternatively, if a user utters a voice message on their tablet saying "I went to the park with my friends today," the analysis unit simultaneously integrates and analyzes step count data from their fitness tracker and provides feedback such as "It looks like you walked a lot. What did you do for fun?" Alternatively, if a user uploads photos taken in the park as image data from their computer, the analysis unit simultaneously integrates and analyzes location information data from their smartphone and provides feedback such as "I see you like this park. Is there a place you particularly like?" This allows the analysis unit to provide feedback by integrating data from different devices.

[0036] The analysis unit can refer to external data sources and provide related information. For example, when a user inputs, "Today was a really fun day," the generation AI refers to the news for that day and provides feedback such as, "There was a lot of particularly good news today. What did you enjoy?" When a user utters, "I went to the park with my friends today," the generation AI refers to the weather forecast for that day and provides feedback such as, "The weather was nice today, wasn't it? What did you do for fun?" When a user uploads a photo taken at the park as image data, the generation AI refers to event information for that day and provides feedback such as, "There was an event at this park. What was it about?" This makes it possible to provide related information by referring to external data sources.

[0037] The proposal generation unit can generate more specific proposals by comparing the user's answers and additional information with past data. For example, when a user answers, "I played soccer with my friends," the generation AI references past data and generates a specific proposal, such as, "Did you enjoy soccer? What other sports do you like?" When a user answers, "I went to a new restaurant today," the generation AI references past data and generates a specific proposal, such as, "How was the new restaurant? Are there any other restaurants you recommend?" When a user answers, "I saw a movie today," the generation AI references past data and generates a specific proposal, such as, "What movie did you see? What other movies do you like?" This makes it possible to generate specific proposals by comparing them with past data.

[0038] The suggestion generation unit can suggest new activities and hobbies based on the user's interests and concerns. For example, when a user answers, "I played soccer with my friends," the generation AI considers the user's interests and concerns and suggests a new activity, such as, "Did you enjoy soccer? Why don't you try basketball next time?" Similarly, when a user answers, "I went to a new restaurant today," the generation AI considers the user's interests and concerns and suggests a new hobby, such as, "What did you think of the new restaurant? Why don't you try a cooking class next time?" Similarly, when a user answers, "I saw a movie today," the generation AI considers the user's interests and concerns and suggests a new activity, such as, "What kind of movie did you see? How about having a movie night next time?" This makes it possible to suggest new activities and hobbies based on the user's interests and concerns.

[0039] The proposal generation unit can generate proposals that incorporate information from different cultures and regions. For example, when a user answers, "I played soccer with my friends," the generation AI incorporates information from different cultures and generates a proposal such as, "Did you enjoy soccer? Why not learn about Brazilian soccer culture next time?" When a user answers, "I went to a new restaurant today," the generation AI incorporates information from different regions and generates a proposal such as, "How was the new restaurant? Why not try an Italian restaurant next time?" When a user answers, "I saw a movie today," the generation AI incorporates information from different cultures and generates a proposal such as, "What kind of movie did you see? Why not try a French movie next time?" In this way, proposals that incorporate information from different cultures and regions can be generated.

[0040] The proposal generation unit can generate proposals by referring to similar experiences and feedback of other users. For example, when a user answers "I played soccer with friends," the generation AI refers to similar experiences of other users and generates a proposal such as "Other users also enjoy soccer. Why don't you try participating in a soccer event next time?". Also, when a user answers "I went to a new restaurant today," the generation AI refers to feedback from other users and generates a proposal such as "Other users also gave this restaurant high marks. Why don't you try going there with your friends next time?". Also, when a user answers "I saw a movie today," the generation AI refers to similar experiences of other users and generates a proposal such as "Other users also enjoyed this movie. Why don't you have a movie night next time?". In this way, proposals can be generated by referring to similar experiences and feedback of other users.

[0041] The diary generation unit can maintain consistency with past data when organizing the user's input content and automatically generating sentences appropriate for the diary format. For example, when a user inputs, "Today, I played soccer with my friends at the park and had a lot of fun," the generation AI references past diary data and automatically generates, while maintaining consistency, "Today, I played soccer with my friends at the park and had a great day." Also, when a user inputs, "Today, I went to a new restaurant," the generation AI references past diary data and automatically generates, while maintaining consistency, "Today, I went to a new restaurant and enjoyed the delicious food." Also, when a user inputs, "Today, I saw a movie," the generation AI references past diary data and automatically generates, while maintaining consistency, "Today, I saw a movie and had a great time." This allows for automatic generation of diaries while maintaining consistency with past data.

[0042] The diary generation unit can automatically generate sentences incorporating expressions from different genres. For example, when a user inputs, "Today, I played soccer with my friends at the park and had a lot of fun," the generation AI converts the content into a poetic expression, automatically generating, "A fun day sharing the joy of soccer with friends at the park." Also, when a user inputs, "Today, I went to a new restaurant," the generation AI converts the content into a narrative expression, automatically generating, "A day of adventure at a new restaurant, delicious food, and new discoveries." Also, when a user inputs, "Today, I saw a movie," the generation AI converts the content into a poetic expression, automatically generating, "A day when I was captivated by the story on the screen." This makes it possible to automatically generate sentences incorporating expressions from different genres.

[0043] The diary generation unit can propose a multimedia diary that combines image and audio data. For example, when a user inputs, "Today, I played soccer with my friends at the park and had a lot of fun," the generation AI combines image and audio data related to the content and proposes a multimedia diary that reads, "Today, I played soccer with my friends at the park and had a really fun day." Also, when a user inputs, "Today, I went to a new restaurant," the generation AI combines image and audio data related to the content and proposes a multimedia diary that reads, "Today, I went to a new restaurant and enjoyed the delicious food." Also, when a user inputs, "Today, I saw a movie," the generation AI combines image and audio data related to the content and proposes a multimedia diary that reads, "Today, I saw a movie and had a great time." In this way, it is possible to propose a multimedia diary that combines image and audio data.

[0044] The continuous support unit continuously monitors the content of the user's diary and changes in emotions, analyzing long-term trends and providing feedback. For example, when a user continues to enter diary entries over several weeks, the generation AI monitors the content and detects an increase in positive emotions, providing feedback such as, "It seems like you've been having a lot of fun lately. Have you found a new hobby?" Similarly, when a user continues to enter diary entries over several months, the generation AI monitors the content and detects an increase in negative emotions, providing feedback such as, "You seem to have been feeling tired lately. Is there anything that's bothering you?" Similarly, when a user continues to enter diary entries over several years, the generation AI monitors the content and detects an increase in positive emotions during certain seasons, providing feedback such as, "I see you like this season. Are there any events you're particularly looking forward to?" This allows the continuous support unit to analyze long-term trends and provide feedback.

[0045] The continuous support unit continuously monitors the contents of the user's diary and changes in emotions, and can provide advice regarding the user's health and mental health. For example, if a user expresses negative emotions such as "I'm tired" for several weeks, the generation AI monitors the content and provides advice such as, "You seem to have been feeling tired lately. Is there something worrying you? I recommend taking some time to relax." Similarly, if a user expresses positive emotions such as "I'm having fun" for several months, the generation AI monitors the content and provides advice such as, "It seems like you've been having a lot of fun lately. Have you found a new hobby? Keep it up." Furthermore, if a user continues to enter diary entries over several years, the generation AI monitors the content and detects an increase in negative emotions during certain seasons. It then provides advice such as, "It seems like you're feeling a little down during this season. I recommend going outside and refreshing yourself." This allows the unit to provide advice regarding the user's health and mental health.

[0046] The continuous support unit continuously monitors the content of the user's diary and changes in emotions, and can provide support tailored to different seasons and events. For example, when a user enters "I went to see the cherry blossoms" in the spring, the generation AI monitors the content and provides support such as "Cherry blossom viewing in the spring is fun. Which park do you plan to go to next time?". Similarly, when a user enters "I went to the beach" in the summer, the generation AI monitors the content and provides support such as "The beach in the summer is fun. Which beach do you plan to go to next time?". Similarly, when a user enters "I went skiing" in the winter, the generation AI monitors the content and provides support such as "Skiing in the winter is fun. Which ski resort do you plan to go to next time?". This makes it possible to provide support tailored to different seasons and events.

[0047] The continuous support unit continuously monitors the user's diary entries and emotional changes, performs comparative analysis with other users, and can propose common challenges and solutions. For example, when a user writes, "I've been feeling tired lately," the generation AI compares the user's diary entries with other users' data and offers suggestions such as, "Other users feel the same. Why not try yoga or meditation to relax?" Similarly, when a user writes, "I've been having a lot of fun lately," the generation AI compares the user's diary entries with other users' data and offers suggestions such as, "Other users feel the same. Why not take a cooking class to find a new hobby?" Similarly, when a user writes, "I've been feeling a lot of stress lately," the generation AI compares the user's diary entries with other users' data and offers suggestions such as, "Other users feel the same. Why not try taking a walk or exercising to relieve stress?" This allows for comparative analysis with other users to propose common challenges and solutions.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The diary support system allows users to receive support from the generation AI when writing their diary, making the content of their diary more enriching. For example, by recording daily events and emotions in detail, users can deepen their self-understanding. In addition, feedback and questions from the generation AI can help users gain new perspectives and insights. Furthermore, the generation AI can provide appropriate feedback and suggestions based on the data entered by the user, supporting the user's self-improvement.

[0050] The data input unit compares the data entered by the user with past input data, detects patterns and trends, and provides feedback. For example, when a user enters, "Today was a really fun day," the generation AI compares it with past input data and detects that similar positive emotions have continued, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, when a user utters, "Today I went to the park with my friends," the generation AI compares it with past voice data and detects that they often hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, when a user uploads photos taken at a park as image data, the generation AI compares it with past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided by comparing it with past data.

[0051] The data input unit supports input in different languages, enabling a multilingual diary system. For example, when a user inputs "Today was a very enjoyable day" in Japanese, the generation AI translates the content into English and displays "I had a very enjoyable day today," providing a multilingual diary system. Also, when a user speaks in Spanish, "I went to the park with my friends today," the generation AI translates the content into French and displays "Je suis alle au park avec des amis aujourd'hui," providing a multilingual diary system. Also, when a user uploads a photo taken in a park as image data and enters a description in Chinese, the generation AI translates the content into German and displays "Dieses Foto wurde im Park aufgenommen," providing a multilingual diary system. This allows a multilingual diary system to be provided.

[0052] The data input unit can automatically tag image and audio data entered by the user, improving searchability. For example, when a user enters "Today was a really fun day," the generation AI automatically tags the content with "fun" and "day," making it easier to search later. Also, when a user says, "Today I went to the park with my friends," the generation AI automatically tags the content with "friends" and "park," making it easier to search later. Also, when a user uploads a photo taken in a park as image data, the generation AI automatically tags the image with "park" and "photo," making it easier to search later. This improves the searchability of data.

[0053] When analyzing user input data, the analysis unit considers correlation with past data, enabling it to provide more specific feedback. For example, if a user inputs, "Today was a really fun day," the generation AI references past data and detects a continuation of similar positive emotions, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, if a user utters, "Today I went to the park with my friends," the generation AI references past voice data and detects that you frequently hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, if a user uploads photos taken at a park as image data, the generation AI references past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided while taking correlation with past data into account.

[0054] The analysis unit can provide personalized feedback that takes into account the user's lifestyle, hobbies, and preferences. For example, when a user inputs, "Today was a really fun day," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "What did you enjoy? Is it related to your recent hobby?" When a user utters, "Today, I went to the park with my friends," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "Oh, you went to the park with your friends. What did you do? Is it related to your recent hobby?" When a user uploads a photo taken at the park as image data, the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "This photo is really beautiful. Which park was it taken in? Is it related to your recent hobby?" This allows the system to provide feedback that takes into account the user's lifestyle, hobbies, and preferences.

[0055] The analysis unit can refer to external data sources and provide related information. For example, when a user inputs, "Today was a really fun day," the generation AI will refer to the news for that day and provide feedback such as, "There was a lot of particularly good news today. What did you enjoy?" When a user utters, "I went to the park with my friends today," the generation AI will refer to the weather forecast for that day and provide feedback such as, "The weather was nice today, wasn't it? What did you do?" When a user uploads a photo taken at the park as image data, the generation AI will refer to the event information for that day and provide feedback such as, "There was an event at this park. What was it about?" This makes it possible to provide related information by referencing external data sources.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data input unit inputs data from the user. For example, the user can input text such as "Today was a really fun day," speak aloud such as "Today I went to the park with my friends," or upload a photo taken in the park as image data. Step 2: The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes text data using a text generation AI (e.g., LLM), analyzes voice data using voice recognition technology, and analyzes image data using image recognition technology. Step 3: The feedback generator generates feedback based on the data analyzed by the analyzer. For example, if a user enters "Today was a really fun day," the generator generates feedback such as "What did you enjoy?" Step 4: The proposal generator generates proposals based on the feedback generated by the feedback generator. For example, if the user answers "I played soccer with my friends," the generation AI generates proposals such as "Did you enjoy soccer? What other sports do you like?"

[0058] (Example 2) In a diary support system according to an embodiment of the present invention, a user communicates diary content, emotions, voice, and image data to an AI model, and the AI ​​model supports the user through appropriate feedback and questions. The user receives answers and suggestions from the AI ​​model and writes the diary content based on these answers and suggestions. This allows the diary support system to receive support from the generation AI when the user writes a diary, thereby enriching the content of the diary. For example, by recording daily events and emotions in detail, the user can deepen self-understanding. Furthermore, the feedback and questions from the generation AI can provide new perspectives and insights.

[0059] A diary support system according to an embodiment includes a data input unit, an analysis unit, a feedback generation unit, and a suggestion generation unit. The data input unit inputs data from a user. For example, the user can input text such as "Today was a really fun day," speak verbally such as "I went to the park with my friends today," or upload images of photos taken in the park. The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes the text data using a text generation AI (e.g., LLM), analyzes the audio data using speech recognition technology, and analyzes the image data using image recognition technology. The feedback generation unit generates feedback based on the data analyzed by the analysis unit. For example, if a user inputs "Today was a really fun day," the generation AI generates feedback such as "What did you enjoy?" The suggestion generation unit generates suggestions based on the feedback generated by the feedback generation unit. For example, if a user answers "I played soccer with my friends," the generation AI generates suggestions such as "Did you enjoy soccer? What other sports do you like?" This allows the diary support system according to an embodiment to analyze user data and provide appropriate feedback and suggestions. For example, when a user writes a diary, they can receive support from the generation AI to enrich the content of the diary.

[0060] The data input unit performs real-time sentiment analysis on text and voice data entered by the user and can provide instant feedback based on the input. For example, when a user enters, "Today was a really fun day," the generation AI performs real-time sentiment analysis, detects positive emotions, and provides instant feedback such as, "What did you enjoy?" When a user utters, "Today I went to the park with my friends," the generation AI analyzes the voice data in real time and performs sentiment analysis, providing feedback such as, "Oh, you went to the park with your friends. What did you do?" When a user uploads a photo taken at the park as image data, the generation AI performs image and sentiment analysis, providing feedback such as, "This photo is really nice. Which park was it taken in?" This allows instant feedback to be provided in response to user input.

[0061] The data input unit compares data entered by the user with past input data, detects patterns and trends, and provides feedback. For example, when a user enters, "Today was a really fun day," the generation AI compares it with past input data and detects that similar positive emotions have continued, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, when a user utters, "I went to the park with my friends today," the generation AI compares it with past voice data and detects that you often hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, when a user uploads photos taken at the park as image data, the generation AI compares it with past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided by comparing it with past data.

[0062] The data input unit uses the emotion estimation function to estimate the user's emotions in real time as they input data and make suggestions to elicit positive emotions. For example, when a user enters, "Today was a really fun day," the generation AI estimates the user's emotions in real time and makes a suggestion to elicit positive emotions, such as, "What was fun about it? Tell me more!" When a user utters, "Today I went to the park with my friends," the generation AI estimates the user's emotions in real time and makes a suggestion to elicit positive emotions, such as, "Oh, you went to the park with your friends. What did you do? Tell me more!" When a user uploads a photo taken at the park as image data, the generation AI estimates the user's emotions in real time and makes a suggestion to elicit positive emotions, such as, "This photo is really beautiful. Which park was it taken in? Please show me any other photos you have!" This allows the system to make suggestions to elicit positive emotions from the user.

[0063] The data input unit supports input in different languages, enabling a multilingual diary system to be provided. For example, when a user inputs "Today was a very enjoyable day" in Japanese, the generation AI translates the content into English and displays "I had a very enjoyable day today," thereby providing a multilingual diary system. Also, when a user speaks in Spanish, "I went to the park with my friends today," the generation AI translates the content into French and displays "Je suis alle au park avec des amis aujourd'hui," thereby providing a multilingual diary system. Also, when a user uploads a photo taken in a park as image data and enters a description of it in Chinese, the generation AI translates the content into German and displays "Dieses Foto wurde im Park aufgenommen," thereby providing a multilingual diary system. In this way, a multilingual diary system can be provided.

[0064] The data input unit can automatically tag image and audio data with data entered by the user, improving searchability. For example, when a user enters "Today was a really fun day," the generation AI automatically tags the content with tags such as "fun" and "day," making it easier to search later. Also, when a user says by voice, "Today I went to the park with my friends," the generation AI automatically tags the content with tags such as "friends" and "park," making it easier to search later. Also, when a user uploads a photo taken in a park as image data, the generation AI automatically tags the image with tags such as "park" and "photo," making it easier to search later. This improves the searchability of data.

[0065] The data input unit uses the emotion estimation function to analyze the emotions entered by the user and suggests music and images that correspond to those emotions, thereby supporting the user's emotions. For example, when a user enters "Today was a really fun day," the generation AI analyzes the positive emotions and suggests upbeat music to further enhance the joyful mood. Also, when a user utters the words "Today I went to the park with my friends," the generation AI analyzes the positive emotions and suggests relaxing landscape images. Also, when a user uploads a photo taken in the park as image data, the generation AI analyzes the positive emotions and similarly suggests images and music to enhance the joyful mood. In this way, the user's emotions can be supported.

[0066] When analyzing user input data, the analysis unit considers correlation with past data, enabling it to provide more specific feedback. For example, when a user inputs, "Today was a really fun day," the generation AI references past data and detects a continuation of similar positive emotions, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, when a user utters, "Today I went to the park with my friends," the generation AI references past voice data and detects that you frequently hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, when a user uploads photos taken at a park as image data, the generation AI references past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided while taking correlation with past data into account.

[0067] The analysis unit can provide personalized feedback that takes into account the user's lifestyle, hobbies, and preferences. For example, when a user inputs, "Today was a really fun day," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "What did you enjoy? Is it related to your recent hobby?" When a user utters, "Today I went to the park with my friends," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "Oh, you went to the park with your friends. What did you do? Is it related to your recent hobby?" When a user uploads a photo taken in a park as image data, the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "This photo is really nice. Which park was it taken in? Is it related to your recent hobby?" This makes it possible to provide feedback that takes into account the user's lifestyle, hobbies, and preferences.

[0068] The analysis unit can integrate data from different devices and provide comprehensive feedback. For example, if a user inputs "Today was a really fun day" on their smartphone, the analysis unit simultaneously integrates and analyzes heart rate data from their smartwatch and provides feedback such as "It looks like your heart rate is up. What did you enjoy?" Alternatively, if a user utters a voice message on their tablet saying "I went to the park with my friends today," the analysis unit simultaneously integrates and analyzes step count data from their fitness tracker and provides feedback such as "It looks like you walked a lot. What did you do for fun?" Alternatively, if a user uploads photos taken in the park as image data from their computer, the analysis unit simultaneously integrates and analyzes location information data from their smartphone and provides feedback such as "I see you like this park. Is there a place you particularly like?" This allows the analysis unit to provide feedback by integrating data from different devices.

[0069] The analysis unit can refer to external data sources and provide related information. For example, when a user inputs, "Today was a really fun day," the generation AI refers to the news for that day and provides feedback such as, "There was a lot of particularly good news today. What did you enjoy?" When a user utters, "I went to the park with my friends today," the generation AI refers to the weather forecast for that day and provides feedback such as, "The weather was nice today, wasn't it? What did you do for fun?" When a user uploads a photo taken at the park as image data, the generation AI refers to event information for that day and provides feedback such as, "There was an event at this park. What was it about?" This makes it possible to provide related information by referring to external data sources.

[0070] The analysis unit uses the emotion estimation function to analyze the emotions of the user's input data and can suggest relaxation and stress relief methods based on the emotions. For example, when a user inputs, "Today was a really fun day," the generation AI analyzes the positive emotion and suggests, "Why not listen to some relaxing music to end a fun day?" as a relaxation method. Similarly, when a user utters, "Today I went to the park with my friends," the generation AI analyzes the positive emotion and suggests, "It looks like you enjoyed spending time with your friends. Why don't you try yoga together next time?" as a stress relief method. Similarly, when a user uploads a photo taken in the park as image data, the generation AI analyzes the positive emotion and suggests, "It looks like you enjoyed spending time in this park. Why don't you try a picnic next time?" as a relaxation method. This allows the analysis unit to suggest relaxation and stress relief methods based on emotions.

[0071] The proposal generation unit can generate more specific proposals by comparing the user's answers and additional information with past data. For example, when a user answers, "I played soccer with my friends," the generation AI references past data and generates a specific proposal, such as, "Did you enjoy soccer? What other sports do you like?" When a user answers, "I went to a new restaurant today," the generation AI references past data and generates a specific proposal, such as, "How was the new restaurant? Are there any other restaurants you recommend?" When a user answers, "I saw a movie today," the generation AI references past data and generates a specific proposal, such as, "What movie did you see? What other movies do you like?" This makes it possible to generate specific proposals by comparing them with past data.

[0072] The suggestion generation unit can suggest new activities and hobbies based on the user's interests and concerns. For example, when a user answers, "I played soccer with my friends," the generation AI considers the user's interests and concerns and suggests a new activity, such as, "Did you enjoy soccer? Why don't you try basketball next time?" Similarly, when a user answers, "I went to a new restaurant today," the generation AI considers the user's interests and concerns and suggests a new hobby, such as, "What did you think of the new restaurant? Why don't you try a cooking class next time?" Similarly, when a user answers, "I saw a movie today," the generation AI considers the user's interests and concerns and suggests a new activity, such as, "What kind of movie did you see? How about having a movie night next time?" This makes it possible to suggest new activities and hobbies based on the user's interests and concerns.

[0073] The suggestion generation unit can use the emotion estimation function to analyze the user's emotions regarding their answers and additional information, and generate suggestions based on those emotions. For example, when a user answers "I played soccer with my friends," the generation AI analyzes the positive emotion and generates a suggestion such as "Did you enjoy soccer? Why don't you invite some friends next time so you can have more fun?" When a user answers "I went to a new restaurant today," the generation AI analyzes the positive emotion and generates a suggestion such as "How was the new restaurant? Why don't you try going with your family next time?" When a user answers "I saw a movie today," the generation AI analyzes the positive emotion and generates a suggestion such as "What kind of movie did you see? Why don't you try watching a movie with your friends next time?" This makes it possible to generate suggestions based on emotions.

[0074] The proposal generation unit can generate proposals that incorporate information from different cultures and regions. For example, when a user answers, "I played soccer with my friends," the generation AI incorporates information from different cultures and generates a proposal such as, "Did you enjoy soccer? Why not learn about Brazilian soccer culture next time?" When a user answers, "I went to a new restaurant today," the generation AI incorporates information from different regions and generates a proposal such as, "How was the new restaurant? Why not try an Italian restaurant next time?" When a user answers, "I saw a movie today," the generation AI incorporates information from different cultures and generates a proposal such as, "What kind of movie did you see? Why not try a French movie next time?" In this way, proposals that incorporate information from different cultures and regions can be generated.

[0075] The proposal generation unit can generate proposals by referring to similar experiences and feedback of other users. For example, when a user answers "I played soccer with friends," the generation AI refers to similar experiences of other users and generates a proposal such as "Other users also enjoy soccer. Why don't you try participating in a soccer event next time?". Also, when a user answers "I went to a new restaurant today," the generation AI refers to feedback from other users and generates a proposal such as "Other users also gave this restaurant high marks. Why don't you try going there with your friends next time?". Also, when a user answers "I saw a movie today," the generation AI refers to similar experiences of other users and generates a proposal such as "Other users also enjoyed this movie. Why don't you have a movie night next time?". In this way, proposals can be generated by referring to similar experiences and feedback of other users.

[0076] The suggestion generation unit uses the emotion estimation function to analyze the user's emotions regarding their answers and additional information, and can suggest travel destinations and events based on their emotions. For example, when a user answers "I played soccer with my friends," the generation AI analyzes the positive emotion and generates a suggestion such as "Did you enjoy soccer? Why don't you plan a trip to watch a soccer game next time?" When a user answers "I went to a new restaurant today," the generation AI analyzes the positive emotion and generates a suggestion such as "How was the new restaurant? Why don't you try attending a gourmet festival next time?" When a user answers "I saw a movie today," the generation AI analyzes the positive emotion and generates a suggestion such as "What kind of movie did you see? Why don't you try attending a film festival next time?" This makes it possible to suggest travel destinations and events based on emotions.

[0077] The diary generation unit can maintain consistency with past data when organizing the user's input content and automatically generating sentences appropriate for the diary format. For example, when a user inputs, "Today, I played soccer with my friends at the park and had a lot of fun," the generation AI references past diary data and automatically generates, while maintaining consistency, "Today, I played soccer with my friends at the park and had a great day." Also, when a user inputs, "Today, I went to a new restaurant," the generation AI references past diary data and automatically generates, while maintaining consistency, "Today, I went to a new restaurant and enjoyed the delicious food." Also, when a user inputs, "Today, I saw a movie," the generation AI references past diary data and automatically generates, while maintaining consistency, "Today, I saw a movie and had a great time." This allows for automatic generation of diaries while maintaining consistency with past data.

[0078] The diary generation unit can use the emotion estimation function to analyze the emotion of the user's input content and suggest sentence expressions based on the emotion. For example, when a user inputs, "Today, I played soccer with my friends at the park and had a lot of fun," the generation AI analyzes the positive emotion and suggests a sentence expression based on the emotion, such as, "Today, I played soccer with my friends at the park and had a really fun day." Also, when a user inputs, "Today, I went to a new restaurant," the generation AI analyzes the positive emotion and suggests a sentence expression based on the emotion, such as, "Today, I went to a new restaurant and enjoyed the delicious food." Also, when a user inputs, "Today, I saw a movie," the generation AI analyzes the positive emotion and suggests a sentence expression based on the emotion, such as, "Today, I saw a movie and had a great time." In this way, sentence expressions based on emotions can be suggested.

[0079] The diary generation unit can automatically generate sentences incorporating expressions from different genres. For example, when a user inputs, "Today, I played soccer with my friends at the park and had a lot of fun," the generation AI converts the content into a poetic expression, automatically generating, "A fun day sharing the joy of soccer with friends at the park." Also, when a user inputs, "Today, I went to a new restaurant," the generation AI converts the content into a narrative expression, automatically generating, "A day of adventure at a new restaurant, delicious food, and new discoveries." Also, when a user inputs, "Today, I saw a movie," the generation AI converts the content into a poetic expression, automatically generating, "A day when I was captivated by the story on the screen." This makes it possible to automatically generate sentences incorporating expressions from different genres.

[0080] The diary generation unit can propose a multimedia diary that combines image and audio data. For example, when a user inputs, "Today, I played soccer with my friends at the park and had a lot of fun," the generation AI combines image and audio data related to the content and proposes a multimedia diary that reads, "Today, I played soccer with my friends at the park and had a really fun day." Also, when a user inputs, "Today, I went to a new restaurant," the generation AI combines image and audio data related to the content and proposes a multimedia diary that reads, "Today, I went to a new restaurant and enjoyed the delicious food." Also, when a user inputs, "Today, I saw a movie," the generation AI combines image and audio data related to the content and proposes a multimedia diary that reads, "Today, I saw a movie and had a great time." In this way, it is possible to propose a multimedia diary that combines image and audio data.

[0081] The diary generation unit uses the emotion estimation function to analyze the emotions of the user's input content and suggests colors and fonts according to the emotions, allowing the emotions to be visually expressed. For example, when a user inputs, "Today I played soccer with my friends at the park and had a lot of fun," the generation AI analyzes the positive emotions and suggests bright colors and fun fonts to visually express the emotions. Also, when a user inputs, "Today I went to a new restaurant," the generation AI analyzes the positive emotions and suggests vivid colors and elegant fonts to visually express the emotions. Also, when a user inputs, "Today I saw a movie," the generation AI analyzes the positive emotions and suggests warm colors and soft fonts to visually express the emotions. In this way, emotions can be visually expressed by suggesting colors and fonts according to the emotions.

[0082] The continuous support unit continuously monitors the content of the user's diary and changes in emotions, analyzing long-term trends and providing feedback. For example, when a user continues to enter diary entries over several weeks, the generation AI monitors the content and detects an increase in positive emotions, providing feedback such as, "It seems like you've been having a lot of fun lately. Have you found a new hobby?" Similarly, when a user continues to enter diary entries over several months, the generation AI monitors the content and detects an increase in negative emotions, providing feedback such as, "You seem to have been feeling tired lately. Is there anything that's bothering you?" Similarly, when a user continues to enter diary entries over several years, the generation AI monitors the content and detects an increase in positive emotions during certain seasons, providing feedback such as, "I see you like this season. Are there any events you're particularly looking forward to?" This allows the continuous support unit to analyze long-term trends and provide feedback.

[0083] The continuous support unit continuously monitors the contents of the user's diary and changes in emotions, and can provide advice regarding the user's health and mental health. For example, if a user expresses negative emotions such as "I'm tired" for several weeks, the generation AI monitors the content and provides advice such as, "You seem to have been feeling tired lately. Is there something worrying you? I recommend taking some time to relax." Similarly, if a user expresses positive emotions such as "I'm having fun" for several months, the generation AI monitors the content and provides advice such as, "It seems like you've been having a lot of fun lately. Have you found a new hobby? Keep it up." Furthermore, if a user continues to enter diary entries over several years, the generation AI monitors the content and detects an increase in negative emotions during certain seasons. It then provides advice such as, "It seems like you're feeling a little down during this season. I recommend going outside and refreshing yourself." This allows the unit to provide advice regarding the user's health and mental health.

[0084] The continuous support unit uses the emotion estimation function to continuously monitor the contents of the user's diary and changes in emotions, and can provide emotion-based support. For example, if a user expresses negative emotions such as "tired" for several weeks, the generation AI monitors the content and uses the emotion estimation function to provide support such as, "You seem to have been feeling tired lately. Is there something worrying you? I recommend taking some time to relax." Similarly, if a user expresses positive emotions such as "having fun" for several months, the generation AI monitors the content and uses the emotion estimation function to provide support such as, "It seems like you've been having a lot of fun lately. Have you found a new hobby? Keep it up." Furthermore, if a user continues to enter diary entries over several years, the generation AI monitors the content and uses the emotion estimation function to detect increases in negative emotions during certain seasons, and provides support such as, "It seems like you're feeling a little down during this season. I recommend going outside and refreshing yourself." This allows for emotion-based support.

[0085] The continuous support unit continuously monitors the content of the user's diary and changes in emotions, and can provide support tailored to different seasons and events. For example, when a user enters "I went to see the cherry blossoms" in the spring, the generation AI monitors the content and provides support such as "Cherry blossom viewing in the spring is fun. Which park do you plan to go to next time?". Similarly, when a user enters "I went to the beach" in the summer, the generation AI monitors the content and provides support such as "The beach in the summer is fun. Which beach do you plan to go to next time?". Similarly, when a user enters "I went skiing" in the winter, the generation AI monitors the content and provides support such as "Skiing in the winter is fun. Which ski resort do you plan to go to next time?". This makes it possible to provide support tailored to different seasons and events.

[0086] The continuous support unit continuously monitors the user's diary entries and emotional changes, performs comparative analysis with other users, and can propose common challenges and solutions. For example, when a user writes, "I've been feeling tired lately," the generation AI compares the user's diary entries with other users' data and offers suggestions such as, "Other users feel the same. Why not try yoga or meditation to relax?" Similarly, when a user writes, "I've been having a lot of fun lately," the generation AI compares the user's diary entries with other users' data and offers suggestions such as, "Other users feel the same. Why not take a cooking class to find a new hobby?" Similarly, when a user writes, "I've been feeling a lot of stress lately," the generation AI compares the user's diary entries with other users' data and offers suggestions such as, "Other users feel the same. Why not try taking a walk or exercising to relieve stress?" This allows for comparative analysis with other users to propose common challenges and solutions.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The diary support system allows users to receive support from the generation AI when writing their diary, making the content of their diary more enriching. For example, by recording daily events and emotions in detail, users can deepen their self-understanding. In addition, feedback and questions from the generation AI can help users gain new perspectives and insights. Furthermore, the generation AI can provide appropriate feedback and suggestions based on the data entered by the user, supporting the user's self-improvement.

[0089] The data input unit performs real-time sentiment analysis on text and voice data entered by the user, and can provide instant feedback based on the input. For example, when a user enters, "Today was a really fun day," the generation AI performs real-time sentiment analysis, detects positive emotions, and provides instant feedback such as, "What did you enjoy?" When a user utters, "Today I went to the park with my friends," the generation AI analyzes the voice data in real time and performs sentiment analysis, providing feedback such as, "Oh, you went to the park with your friends. What did you do?" When a user uploads a photo taken at the park as image data, the generation AI performs image and sentiment analysis, providing feedback such as, "This photo is really beautiful. Which park was it taken in?" This allows instant feedback to be provided in response to user input.

[0090] The data input unit compares the data entered by the user with past input data, detects patterns and trends, and provides feedback. For example, when a user enters, "Today was a really fun day," the generation AI compares it with past input data and detects that similar positive emotions have continued, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, when a user utters, "Today I went to the park with my friends," the generation AI compares it with past voice data and detects that they often hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, when a user uploads photos taken at a park as image data, the generation AI compares it with past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided by comparing it with past data.

[0091] The data input unit uses the emotion estimation function to estimate the user's emotions in real time as they input data and make suggestions to elicit positive emotions. For example, when a user enters, "Today was a really fun day," the generation AI estimates the emotion in real time and makes a suggestion to elicit positive emotions, such as, "What was fun about it? Tell me more!". Similarly, when a user utters, "Today I went to the park with my friends," the generation AI estimates the emotion in real time and makes a suggestion to elicit positive emotions, such as, "Oh, you went to the park with your friends. What did you do? Tell me more!". Similarly, when a user uploads a photo taken at the park as image data, the generation AI estimates the emotion in real time and makes a suggestion to elicit positive emotions, such as, "This photo is really beautiful. Which park was it taken in? Please show me any other photos you have!". This allows for suggestions to elicit positive emotions from the user.

[0092] The data input unit supports input in different languages, enabling a multilingual diary system. For example, when a user inputs "Today was a very enjoyable day" in Japanese, the generation AI translates the content into English and displays "I had a very enjoyable day today," providing a multilingual diary system. Also, when a user speaks in Spanish, "I went to the park with my friends today," the generation AI translates the content into French and displays "Je suis alle au park avec des amis aujourd'hui," providing a multilingual diary system. Also, when a user uploads a photo taken in a park as image data and enters a description in Chinese, the generation AI translates the content into German and displays "Dieses Foto wurde im Park aufgenommen," providing a multilingual diary system. This allows a multilingual diary system to be provided.

[0093] The data input unit can automatically tag image and audio data entered by the user, improving searchability. For example, when a user enters "Today was a really fun day," the generation AI automatically tags the content with "fun" and "day," making it easier to search later. Also, when a user says, "Today I went to the park with my friends," the generation AI automatically tags the content with "friends" and "park," making it easier to search later. Also, when a user uploads a photo taken in a park as image data, the generation AI automatically tags the image with "park" and "photo," making it easier to search later. This improves the searchability of data.

[0094] The data input unit uses the emotion estimation function to analyze the emotions the user enters and suggests music and images that correspond to those emotions, thereby supporting the user's emotions. For example, when a user enters "Today was a really fun day," the generation AI analyzes the positive emotion and suggests upbeat music to further enhance the joyful mood. Similarly, when a user utters the words "Today I went to the park with my friends," the generation AI analyzes the positive emotion and suggests relaxing landscape images. Similarly, when a user uploads a photo taken in the park as image data, the generation AI analyzes the positive emotion and suggests images and music that will similarly enhance the joyful mood. In this way, the user's emotions can be supported.

[0095] When analyzing user input data, the analysis unit considers correlation with past data, enabling it to provide more specific feedback. For example, if a user inputs, "Today was a really fun day," the generation AI references past data and detects a continuation of similar positive emotions, providing feedback such as, "It seems like you've had a lot of fun lately. Have you found a new hobby?" Similarly, if a user utters, "Today I went to the park with my friends," the generation AI references past voice data and detects that you frequently hang out with the same friends, providing feedback such as, "It seems like you hang out with your friends a lot. What kind of activities do you like best?" Similarly, if a user uploads photos taken at a park as image data, the generation AI references past image data and detects that many photos are taken at the same park, providing feedback such as, "I see you like this park. Do you have a favorite spot?" This allows feedback to be provided while taking correlation with past data into account.

[0096] The analysis unit can provide personalized feedback that takes into account the user's lifestyle, hobbies, and preferences. For example, when a user inputs, "Today was a really fun day," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "What did you enjoy? Is it related to your recent hobby?" When a user utters, "Today, I went to the park with my friends," the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "Oh, you went to the park with your friends. What did you do? Is it related to your recent hobby?" When a user uploads a photo taken at the park as image data, the generation AI takes into account the user's lifestyle, hobbies, and preferences and provides feedback such as, "This photo is really beautiful. Which park was it taken in? Is it related to your recent hobby?" This allows the system to provide feedback that takes into account the user's lifestyle, hobbies, and preferences.

[0097] The analysis unit can refer to external data sources and provide related information. For example, when a user inputs, "Today was a really fun day," the generation AI will refer to the news for that day and provide feedback such as, "There was a lot of particularly good news today. What did you enjoy?" When a user utters, "I went to the park with my friends today," the generation AI will refer to the weather forecast for that day and provide feedback such as, "The weather was nice today, wasn't it? What did you do?" When a user uploads a photo taken at the park as image data, the generation AI will refer to the event information for that day and provide feedback such as, "There was an event at this park. What was it about?" This makes it possible to provide related information by referencing external data sources.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The data input unit inputs data from the user. For example, the user can input text such as "Today was a really fun day," speak aloud such as "Today I went to the park with my friends," or upload a photo taken in the park as image data. Step 2: The analysis unit analyzes the data input by the data input unit. For example, the generation AI analyzes text data using a text generation AI (e.g., LLM), analyzes voice data using voice recognition technology, and analyzes image data using image recognition technology. Step 3: The feedback generator generates feedback based on the data analyzed by the analyzer. For example, if a user enters "Today was a really fun day," the generator generates feedback such as "What did you enjoy?" Step 4: The proposal generator generates proposals based on the feedback generated by the feedback generator. For example, if the user answers "I played soccer with my friends," the generation AI generates proposals such as "Did you enjoy soccer? What other sports do you like?"

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data input unit for inputting data from a user; an analysis unit that analyzes the data input by the data input unit; a feedback generation unit that generates feedback based on the data analyzed by the analysis unit; a proposal generation unit that generates a proposal based on the feedback generated by the feedback generation unit. A system characterized by:

2. The data input unit The system performs real-time sentiment analysis on the text and voice data entered by the user, providing immediate feedback based on the input.

2. The system of claim 1.

3. The data input unit Compare the data entered by the user with past input data to detect patterns and trends and provide feedback 2. The system of claim 1.

4. The data input unit The system estimates the user's emotions in real time as they input, and makes suggestions to elicit positive emotions.

2. The system of claim 1.

5. The data input unit Supports input in different languages ​​and provides a multilingual diary system 2. The system of claim 1.

6. The data input unit Automatically tagging images and audio data with the data entered by the user to improve searchability 2. The system of claim 1.

Images