System

Abstract

An object of a system according to an embodiment is to analyze the content of a diary of a user and provide appropriate feedback and suggestions.SOLUTION: A system includes a diary analysis part, a comment generation part, and a proposal part. When the user writes a diary, the diary analysis unit analyzes the content of the diary. The comment generation unit generates an appropriate comment based on the content analyzed by the diary analysis unit. The suggestion unit suggests the user's latent need based on the comment generated by the comment 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 analyze the contents of a user's diary and provide appropriate feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the contents of a user's diary and provide appropriate feedback and suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a diary analysis unit, a comment generation unit, and a suggestion unit. When a user writes a diary, the diary analysis unit analyzes the content of the diary. The comment generation unit generates an appropriate comment based on the content analyzed by the diary analysis unit. The suggestion unit suggests potential needs of the user based on the comment generated by the comment generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the contents of a user's diary and provide appropriate feedback and suggestions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​diary service according to the embodiment of the present invention is a system in which a user writes a diary and the generation AI learns from the content and understands the user. As a result, the AI ​​diary service can enrich the user's life by analyzing the content of the user's diary and providing appropriate comments and suggestions.

[0029] An AI diary service according to an embodiment includes a diary analysis unit, a comment generation unit, and a suggestion unit. The diary analysis unit analyzes the content of a diary entry written by a user. For example, the diary analysis unit uses a generation AI to analyze the user's writing and interpret their hobbies, preferences, personality, and psychological state. The diary analysis unit can also use natural language processing technology to understand the meaning of the writing and analyze the user's emotions. For example, the generation AI analyzes the writing using a text generation AI (e.g., LLM) to estimate the user's emotions. The generation AI can also analyze image and audio data using a multimodal generation AI. The comment generation unit generates appropriate comments based on the content analyzed by the diary analysis unit. For example, if a user writes, "I failed at work today. I'm feeling really down," the comment generation unit can provide encouraging words such as, "Don't worry, everyone makes mistakes. I'm sure you'll do better next time." If a user writes, "I started a new hobby today. It's so much fun," the comment generation unit can also provide compliments such as, "It's great that you found a new hobby. Have fun!" The suggestion unit suggests potential needs of the user based on the comments generated by the comment generation unit. For example, if a user writes, "I feel like I haven't been getting enough exercise lately," the suggestion unit may suggest, "Why don't you join a nearby gym?". Also, if a user writes, "I want to read a new book," the suggestion unit may suggest, "I recommend this book." As a result, the AI ​​diary service according to the embodiment can enrich the user's life by analyzing the contents of the user's diary and providing appropriate comments and suggestions. For example, the user can reaffirm their hobbies and personality through the diary, and be encouraged by the AI's comments, thereby increasing their motivation to continue writing the diary. Furthermore, the AI ​​can read the user's potential needs and suggest appropriate services, making the user's life more convenient and fulfilling.

[0030] The diary analysis unit allows the generation AI to make advanced use of natural language processing technology to understand subtle nuances and context from the user's writing. The diary analysis unit allows the generation AI to make advanced use of natural language processing technology to understand subtle nuances and context from the user's writing. For example, the generation AI analyzes metaphors and similes to understand the deeper meaning of the text. The generation AI can also analyze surrounding text and related topics to understand the context. For example, the generation AI analyzes the context of the text to understand the context. The generation AI can also refer to related background information to understand the meaning of the text. For example, the generation AI collects related news articles and academic papers and refers to them to understand the context. This enables more accurate analysis by understanding subtle nuances and context from the user's writing.

[0031] When analyzing the diary content, the diary analysis unit can simultaneously analyze images and audio data posted by the user, thereby utilizing multimodal information. For example, when analyzing the diary content, the diary analysis unit can simultaneously analyze images and audio data posted by the user, thereby building a system that utilizes multimodal information. For example, the image content is analyzed using image recognition technology. The diary analysis unit can also analyze audio data using voice recognition technology and convert it into text data. For example, voice data is converted into text data using voice recognition technology, and the text data is integrated with the diary content for analysis. The diary analysis unit can also analyze image and audio data and integrate it with the text data for evaluation. For example, image recognition technology is used to analyze visual information and reflect it in the evaluation of the diary content. This allows a deeper understanding of the diary content by simultaneously analyzing the images and audio data posted by the user.

[0032] The diary analysis unit can analyze the diaries of users from different cultural backgrounds and languages ​​to deepen understanding from a global perspective. The diary analysis unit, for example, analyzes the diaries of users from different cultural backgrounds and languages ​​to build a system that deepens understanding from a global perspective. For example, it analyzes the contents of the diary using multilingual natural language processing technology. The diary analysis unit can also understand the diary contents by referring to background information from different cultural backgrounds. For example, it can collect news articles and academic papers from different cultural backgrounds to help understand the diary contents. The diary analysis unit can also analyze the diaries of users from different languages ​​and evaluate them taking language differences into consideration. For example, it can translate the diary contents using a multilingual translation engine and reflect this in the evaluation. In this way, analyzing the diaries of users from different cultural backgrounds and languages ​​deepens understanding from a global perspective.

[0033] The comment generation unit can refer to the content of the user's past diary entries and provide comments that associate past events with the current situation. The comment generation unit, for example, builds a system that refers to the content of the user's past diary entries and provides comments that associate past events with the current situation. For example, it can provide encouraging comments based on past successful experiences. The comment generation unit can also analyze the content of the past diary entries and provide information related to the current situation. For example, it can provide information related to the current situation based on past hobbies and interests. The comment generation unit can also develop an algorithm for associating past events with the current situation. For example, it can use natural language processing technology to associate past events with the current situation. This enables more personalized feedback by providing comments that associate the user's past events with the current situation.

[0034] The comment generation unit allows the generation AI to utilize psychological data to gain a deep understanding of the user's psychological state and select the words most appropriate for that state. The comment generation unit, for example, builds a system in which the generation AI utilizes psychological data to gain a deep understanding of the user's psychological state and select the words most appropriate for that state. For example, the comment generation unit generates comments based on psychological theory. The comment generation unit can also analyze the user's psychological state and provide comments according to that state. For example, if the user is feeling stressed, it can provide advice on how to relax. The comment generation unit can also utilize psychological data to develop an algorithm for evaluating the user's psychological state. For example, the comment generation unit evaluates the user's psychological state based on psychological data and generates appropriate comments. This allows for a deep understanding of the user's psychological state and selects the words most appropriate for that state, enabling more effective feedback.

[0035] The comment generation unit can customize the content of the comment based on the user's hobbies and interests to provide more personalized feedback. The comment generation unit, for example, builds a system that customizes the content of the comment based on the user's hobbies and interests to provide more personalized feedback. For example, it provides information related to the user's hobbies. The comment generation unit can also adjust the content of the comment based on the user's interests. For example, if the user is interested in music, it provides comments related to music. The comment generation unit can also analyze the user's hobbies and interests and develop an algorithm for customizing the content of the comment. For example, it adjusts the content of the comment based on the user's hobbies and interests. This enables more personalized feedback by customizing the comments based on the user's hobbies and interests.

[0036] The comment generation unit can automatically translate appropriate comments for users of different languages ​​and cultural backgrounds, enabling global support. The comment generation unit, for example, builds a system that automatically translates appropriate comments for users of different languages ​​and cultural backgrounds, enabling global support. For example, a multilingual translation engine is utilized. The comment generation unit can also refer to background information from different cultural backgrounds to provide appropriate comments. For example, news articles and academic papers from different cultural backgrounds are collected and reflected in the content of comments. The comment generation unit can also develop an algorithm for providing comments to users of different languages, taking language differences into consideration. For example, comments can be translated using multilingual natural language processing technology and reflected in the evaluation. This enables global support by automatically translating appropriate comments for users of different languages ​​and cultural backgrounds.

[0037] The suggestion unit can analyze a user's diary data, discover long-term trends and patterns, and predict latent needs based on the discovered trends. The suggestion unit, for example, builds a system that analyzes a user's diary data and discovers long-term trends and patterns. For example, it classifies topics for each diary entry and analyzes trends along a time axis. The suggestion unit can also predict a user's latent needs based on trends and patterns. For example, it analyzes trend fluctuations and predicts user needs. The suggestion unit can also develop an algorithm for discovering long-term trends and patterns. For example, it uses a topic model to analyze trends and predict latent needs. In this way, it is possible to predict latent needs by analyzing a user's diary data and discovering long-term trends and patterns.

[0038] The suggestion unit can integrate the user's behavioral history and purchase history with the diary data to make more accurate suggestions based on user needs. The suggestion unit, for example, integrates the user's behavioral history and purchase history with the diary data to build a system that makes more accurate suggestions based on user needs. For example, it suggests related products based on the purchase history. The suggestion unit can also analyze the behavioral history to predict user needs. For example, it analyzes patterns in the behavioral history to predict needs. The suggestion unit can also develop an algorithm for integrating the behavioral history and purchase history with the diary data. For example, it integrates the behavioral history and diary data to build a model for predicting needs. In this way, by integrating the user's behavioral history and purchase history with the diary data, more accurate suggestions based on user needs can be made.

[0039] The proposal unit can integrate diary data with data from different industries or fields to propose new services. The proposal unit, for example, builds a system that integrates diary data with data from different industries or fields to propose new services. For example, it integrates medical data with diary data to propose a health management service. The proposal unit can also analyze data from different industries or fields to predict user needs. For example, it can analyze trends in different industries to predict user needs. The proposal unit can also develop an algorithm for integrating data from different industries or fields. For example, it can integrate data from different industries and build a model for predicting needs. This makes it possible to propose new services by integrating diary data with data from different industries or fields.

[0040] The suggestion unit can share the user's need proposals with other users and connect users who have common needs. The suggestion unit, for example, builds a system that shares the user's need proposals with other users and connects users who have common needs. For example, it matches users who have the same hobbies. The suggestion unit can also develop an algorithm for connecting users who have common needs. For example, it can build a model for identifying and matching users who have common needs. The suggestion unit can also provide a platform for sharing need proposals. For example, it can provide a community function that allows users who have common needs to interact with each other. In this way, by sharing the user's need proposals with other users, users who have common needs can be connected with each other.

[0041] The system can encrypt the user's diary data and introduce the latest encryption technology to strengthen data protection. For example, the system is constructed to encrypt the user's diary data and introduce the latest encryption technology to strengthen data protection. For example, AES-256 encryption technology is used. The system can also introduce technology for securely storing the encrypted data. For example, the encrypted data is securely stored in cloud storage. The system can also regularly update the encryption technology and introduce the latest security technology. For example, the encryption algorithm can be regularly updated to strengthen security. In this way, security can be ensured by encrypting the user's diary data and strengthening data protection.

[0042] The system can record detailed logs of data access and enable the detection and tracing of unauthorized access. For example, the system builds a system that records detailed logs of data access and enables the detection and tracing of unauthorized access. For example, the access logs are monitored in real time to detect abnormal access. The system can also introduce technology for tracing unauthorized access. For example, the access logs are analyzed to identify the source of the unauthorized access. The system can also introduce technology for safely storing the access logs. For example, the access logs are encrypted and safely stored. This makes it possible to record detailed logs of data access and enable the detection and tracing of unauthorized access, thereby strengthening security.

[0043] The system can strengthen data protection across different devices and platforms, allowing users to use their diaries safely from anywhere. For example, the system builds a system that strengthens data protection across different devices and platforms, allowing users to use their diaries safely from anywhere. For example, cloud storage is encrypted. The system can also introduce technology for securely synchronizing data across different devices and platforms. For example, data synchronization is encrypted. The system can also develop an algorithm for strengthening data protection across different devices and platforms. For example, a protocol is built for securely transferring data between devices. This strengthens data protection across different devices and platforms, allowing users to use their diaries safely from anywhere.

[0044] The system can periodically provide security-related information to enhance users' security awareness. For example, the system can be constructed to periodically provide security-related information to enhance users' security awareness. For example, security news and tips can be periodically distributed. The system can also provide a platform for providing security-related information. For example, a website that aggregates security-related information can be provided. The system can also provide an educational program to enhance users' security awareness. For example, an online course on security can be provided. In this way, security awareness can be improved by periodically providing security-related information to enhance users' security awareness.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The diary analysis unit can also monitor the user's health condition when analyzing the contents of the user's diary. For example, if the user writes in the diary that "I've been feeling tired lately," the diary analysis unit can evaluate the user's health condition based on that information and, if necessary, suggest that the user visit a medical institution. The diary analysis unit can also analyze the user's descriptions of diet and exercise and provide advice on maintaining a healthy lifestyle. Furthermore, the diary analysis unit can analyze the user's descriptions of sleep patterns and make suggestions for improving sleep quality. In this way, by analyzing the contents of the user's diary, it is possible to monitor the user's health condition and provide appropriate advice.

[0047] The diary analysis unit can analyze the contents of a user's diary and support the development of the user's creativity and ideas. For example, if a user writes an idea for a new project in their diary, the diary analysis unit can analyze the idea and provide related resources and reference materials. The diary analysis unit can also provide more specific advice by referring to the user's past ideas and projects and relating them to their current idea. Furthermore, the diary analysis unit can suggest brainstorming techniques and creative thinking methods to support the development of the user's ideas. In this way, analyzing the contents of a user's diary can support the development of the user's creativity and ideas.

[0048] The diary analysis unit can analyze the contents of a user's diary and support the user's learning and skill improvement. For example, if a user writes in their diary that they want to learn a new language, the diary analysis unit can suggest appropriate learning resources and materials based on that information. The diary analysis unit can also refer to the user's past learning experiences and provide advice related to their current learning goals. Furthermore, the diary analysis unit can monitor the user's learning progress and make suggestions to improve their learning methods and increase their motivation as needed. In this way, analyzing the contents of a user's diary can support learning and skill improvement.

[0049] The diary analysis unit can analyze the contents of a user's diary and make suggestions to strengthen the user's social connections. For example, if a user writes in their diary that they want to "interact more with friends," the diary analysis unit can use that information to suggest events or activities to promote interaction with friends. The diary analysis unit can also refer to the user's past social activities and provide advice tailored to the user's current situation. Furthermore, the diary analysis unit can suggest online communities or group activities to strengthen the user's social connections. In this way, by analyzing the contents of a user's diary, suggestions to strengthen social connections can be made.

[0050] The diary analysis unit can analyze the contents of a user's diary and make suggestions to support the user's career development. For example, if a user writes in their diary that they are "considering a career change," the diary analysis unit can provide resources and advice that are useful for that career change based on that information. The diary analysis unit can also refer to the user's past work experience and skills to make suggestions related to their current career goals. Furthermore, the diary analysis unit can suggest networking opportunities and specialized training programs to support the user's career development. In this way, by analyzing the contents of a user's diary, suggestions to support career development can be made.

[0051] The diary analysis unit can analyze the contents of a user's diary and suggest new activities based on the user's hobbies and interests. For example, if a user writes in their diary that they want to find a new hobby, the diary analysis unit can use that information to suggest hobbies and activities that match the user's interests. The diary analysis unit can also refer to the user's past hobbies and interests to make suggestions based on the user's current situation. Furthermore, the diary analysis unit can introduce related events and communities based on the user's hobbies and interests. In this way, analyzing the user's diary content can suggest new activities and broaden the user's hobbies and interests.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: When a user writes a diary, the diary analysis unit analyzes its contents. For example, it uses generation AI to analyze the user's writing and read their hobbies, preferences, personality, and psychological state. It can also use natural language processing technology to understand the meaning of the writing and analyze the user's emotions. It can also use text generation AI (e.g., LLM) to analyze the writing and estimate the user's emotions. It is also possible to use multimodal generation AI to analyze image and audio data. Step 2: The comment generation unit generates appropriate comments based on the content analyzed by the diary analysis unit. For example, if a user writes, "I failed at work today. I'm feeling really down," the unit can provide encouraging words such as, "Don't worry, everyone makes mistakes. I'm sure you'll do better next time." Also, if a user writes, "I started a new hobby today. It's so much fun," the unit can provide compliments such as, "It's great that you're finding a new hobby. Have fun." Step 3: The suggestion unit suggests potential needs of the user based on the comments generated by the comment generation unit. For example, if the user writes, "I feel like I haven't been getting enough exercise lately," the suggestion unit may make a suggestion such as, "Why don't you sign up for a membership at a nearby gym?". Also, if the user writes, "I want to read a new book," the suggestion unit may make a suggestion such as, "I recommend this book."

[0054] (Example 2) The AI ​​diary service according to the embodiment of the present invention is a system in which a user writes a diary and the generation AI learns from the content and understands the user. As a result, the AI ​​diary service can enrich the user's life by analyzing the content of the user's diary and providing appropriate comments and suggestions.

[0055] An AI diary service according to an embodiment includes a diary analysis unit, a comment generation unit, and a suggestion unit. The diary analysis unit analyzes the content of a diary entry written by a user. For example, the diary analysis unit uses a generation AI to analyze the user's writing and interpret their hobbies, preferences, personality, and psychological state. The diary analysis unit can also use natural language processing technology to understand the meaning of the writing and analyze the user's emotions. For example, the generation AI analyzes the writing using a text generation AI (e.g., LLM) to estimate the user's emotions. The generation AI can also analyze image and audio data using a multimodal generation AI. The comment generation unit generates appropriate comments based on the content analyzed by the diary analysis unit. For example, if a user writes, "I failed at work today. I'm feeling really down," the comment generation unit can provide encouraging words such as, "Don't worry, everyone makes mistakes. I'm sure you'll do better next time." If a user writes, "I started a new hobby today. It's so much fun," the comment generation unit can also provide compliments such as, "It's great that you found a new hobby. Have fun!" The suggestion unit suggests potential needs of the user based on the comments generated by the comment generation unit. For example, if a user writes, "I feel like I haven't been getting enough exercise lately," the suggestion unit may suggest, "Why don't you join a nearby gym?". Also, if a user writes, "I want to read a new book," the suggestion unit may suggest, "I recommend this book." As a result, the AI ​​diary service according to the embodiment can enrich the user's life by analyzing the contents of the user's diary and providing appropriate comments and suggestions. For example, the user can reaffirm their hobbies and personality through the diary, and be encouraged by the AI's comments, thereby increasing their motivation to continue writing the diary. Furthermore, the AI ​​can read the user's potential needs and suggest appropriate services, making the user's life more convenient and fulfilling.

[0056] The diary analysis unit can analyze the contents of a user's diary, track changes in emotions over the long term, and analyze emotional patterns. The diary analysis unit, for example, analyzes the contents of a user's diary and builds a system that tracks changes in emotions over the long term. For example, it calculates an emotion score for each diary entry and graphs changes in emotions over time. The diary analysis unit can also analyze fluctuations in the emotion score and extract specific patterns to analyze emotional patterns. For example, it identifies the user's emotional patterns based on fluctuations in the emotion score and analyzes long-term changes in emotions. This allows for a deeper understanding by tracking changes in the user's emotions over the long term and analyzing emotional patterns.

[0057] The diary analysis unit allows the generation AI to make advanced use of natural language processing technology to understand subtle nuances and context from the user's writing. The diary analysis unit allows the generation AI to make advanced use of natural language processing technology to understand subtle nuances and context from the user's writing. For example, the generation AI analyzes metaphors and similes to understand the deeper meaning of the text. The generation AI can also analyze surrounding text and related topics to understand the context. For example, the generation AI analyzes the context of the text to understand the context. The generation AI can also refer to related background information to understand the meaning of the text. For example, the generation AI collects related news articles and academic papers and refers to them to understand the context. This enables more accurate analysis by understanding subtle nuances and context from the user's writing.

[0058] The diary analysis unit uses the emotion estimation function to analyze the user's emotions in real time and can deeply understand the diary content based on those emotions. The diary analysis unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and builds a system that deeply understands the diary content based on those emotions. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The diary analysis unit can also extract important parts of the diary content based on the emotion score and deeply understand them. For example, it can focus on analyzing parts with high emotion scores to deeply understand the diary content. In this way, by analyzing the user's emotions in real time and deeply understanding the diary content based on those emotions, more appropriate comments and suggestions can be made.

[0059] When analyzing the diary content, the diary analysis unit can simultaneously analyze images and audio data posted by the user, thereby utilizing multimodal information. For example, when analyzing the diary content, the diary analysis unit can simultaneously analyze images and audio data posted by the user, thereby building a system that utilizes multimodal information. For example, the image content is analyzed using image recognition technology. The diary analysis unit can also analyze audio data using voice recognition technology and convert it into text data. For example, voice data is converted into text data using voice recognition technology, and the text data is integrated with the diary content for analysis. The diary analysis unit can also analyze image and audio data and integrate it with the text data for evaluation. For example, image recognition technology is used to analyze visual information and reflect it in the evaluation of the diary content. This allows a deeper understanding of the diary content by simultaneously analyzing the images and audio data posted by the user.

[0060] The diary analysis unit can analyze the diaries of users from different cultural backgrounds and languages ​​to deepen understanding from a global perspective. The diary analysis unit, for example, analyzes the diaries of users from different cultural backgrounds and languages ​​to build a system that deepens understanding from a global perspective. For example, it analyzes the contents of the diary using multilingual natural language processing technology. The diary analysis unit can also understand the diary contents by referring to background information from different cultural backgrounds. For example, it can collect news articles and academic papers from different cultural backgrounds to help understand the diary contents. The diary analysis unit can also analyze the diaries of users from different languages ​​and evaluate them taking language differences into consideration. For example, it can translate the diary contents using a multilingual translation engine and reflect this in the evaluation. In this way, analyzing the diaries of users from different cultural backgrounds and languages ​​deepens understanding from a global perspective.

[0061] The diary analysis unit can use the emotion estimation function to share diary content based on the user's emotions with other users and promote empathy. The diary analysis unit, for example, uses the emotion estimation function to build a system that shares diary content based on the user's emotions with other users and promotes empathy. For example, diaries with high emotion scores are shared. The diary analysis unit can also generate comments to promote empathy based on the emotion scores. For example, comments showing empathy are provided for parts with high emotion scores. The diary analysis unit can also provide a platform for sharing diary content with other users. For example, a community function to promote empathy is provided, allowing users to share diary content with each other. In this way, empathy can be promoted by sharing diary content based on the user's emotions with other users.

[0062] The comment generation unit can refer to the content of the user's past diary entries and provide comments that associate past events with the current situation. The comment generation unit, for example, builds a system that refers to the content of the user's past diary entries and provides comments that associate past events with the current situation. For example, it can provide encouraging comments based on past successful experiences. The comment generation unit can also analyze the content of the past diary entries and provide information related to the current situation. For example, it can provide information related to the current situation based on past hobbies and interests. The comment generation unit can also develop an algorithm for associating past events with the current situation. For example, it can use natural language processing technology to associate past events with the current situation. This enables more personalized feedback by providing comments that associate the user's past events with the current situation.

[0063] The comment generation unit allows the generation AI to utilize psychological data to gain a deep understanding of the user's psychological state and select the words most appropriate for that state. The comment generation unit, for example, builds a system in which the generation AI utilizes psychological data to gain a deep understanding of the user's psychological state and select the words most appropriate for that state. For example, the comment generation unit generates comments based on psychological theory. The comment generation unit can also analyze the user's psychological state and provide comments according to that state. For example, if the user is feeling stressed, it can provide advice on how to relax. The comment generation unit can also utilize psychological data to develop an algorithm for evaluating the user's psychological state. For example, the comment generation unit evaluates the user's psychological state based on psychological data and generates appropriate comments. This allows for a deep understanding of the user's psychological state and selects the words most appropriate for that state, enabling more effective feedback.

[0064] The comment generation unit can customize the content of the comment based on the user's hobbies and interests to provide more personalized feedback. The comment generation unit, for example, builds a system that customizes the content of the comment based on the user's hobbies and interests to provide more personalized feedback. For example, it provides information related to the user's hobbies. The comment generation unit can also adjust the content of the comment based on the user's interests. For example, if the user is interested in music, it provides comments related to music. The comment generation unit can also analyze the user's hobbies and interests and develop an algorithm for customizing the content of the comment. For example, it adjusts the content of the comment based on the user's hobbies and interests. This enables more personalized feedback by customizing the comments based on the user's hobbies and interests.

[0065] The comment generation unit can automatically translate appropriate comments for users of different languages ​​and cultural backgrounds, enabling global support. The comment generation unit, for example, builds a system that automatically translates appropriate comments for users of different languages ​​and cultural backgrounds, enabling global support. For example, a multilingual translation engine is utilized. The comment generation unit can also refer to background information from different cultural backgrounds to provide appropriate comments. For example, news articles and academic papers from different cultural backgrounds are collected and reflected in the content of comments. The comment generation unit can also develop an algorithm for providing comments to users of different languages, taking language differences into consideration. For example, comments can be translated using multilingual natural language processing technology and reflected in the evaluation. This enables global support by automatically translating appropriate comments for users of different languages ​​and cultural backgrounds.

[0066] The comment generation unit can use the emotion estimation function to share comments based on the user's emotions with other users and promote empathy within a community. The comment generation unit, for example, uses the emotion estimation function to share comments based on the user's emotions with other users and build a system to promote empathy within a community. For example, comments with high emotion scores are shared. The comment generation unit can also generate comments to promote empathy based on the emotion scores. For example, comments showing empathy are provided for parts with high emotion scores. The comment generation unit can also provide a platform for sharing comments with other users. For example, a community function for promoting empathy is provided, allowing users to share comments with each other. In this way, empathy can be promoted within a community by sharing comments based on the user's emotions with other users.

[0067] The suggestion unit can analyze a user's diary data, discover long-term trends and patterns, and predict latent needs based on the discovered trends. The suggestion unit, for example, builds a system that analyzes a user's diary data and discovers long-term trends and patterns. For example, it classifies topics for each diary entry and analyzes trends along a time axis. The suggestion unit can also predict a user's latent needs based on trends and patterns. For example, it analyzes trend fluctuations and predicts user needs. The suggestion unit can also develop an algorithm for discovering long-term trends and patterns. For example, it uses a topic model to analyze trends and predict latent needs. In this way, it is possible to predict latent needs by analyzing a user's diary data and discovering long-term trends and patterns.

[0068] The suggestion unit can integrate the user's behavioral history and purchase history with the diary data to make more accurate suggestions based on user needs. The suggestion unit, for example, integrates the user's behavioral history and purchase history with the diary data to build a system that makes more accurate suggestions based on user needs. For example, it suggests related products based on the purchase history. The suggestion unit can also analyze the behavioral history to predict user needs. For example, it analyzes patterns in the behavioral history to predict needs. The suggestion unit can also develop an algorithm for integrating the behavioral history and purchase history with the diary data. For example, it integrates the behavioral history and diary data to build a model for predicting needs. In this way, by integrating the user's behavioral history and purchase history with the diary data, more accurate suggestions based on user needs can be made.

[0069] The suggestion unit can use the emotion estimation function to analyze needs based on the user's emotions in real time and make instantaneous suggestions. The suggestion unit, for example, uses the emotion estimation function to build a system that analyzes needs based on the user's emotions in real time and makes instantaneous suggestions. For example, the suggestion unit suggests appropriate services based on the emotion score. The suggestion unit can also develop an algorithm for responding quickly to changes in emotions. For example, the suggestion unit can analyze fluctuations in the emotion score and make instantaneous suggestions. The suggestion unit can also introduce technology for analyzing needs in real time. For example, the suggestion unit can analyze emotions in real time and make instantaneous suggestions. This makes it possible to provide more appropriate services by analyzing needs based on the user's emotions in real time and making instantaneous suggestions.

[0070] The proposal unit can integrate diary data with data from different industries or fields to propose new services. The proposal unit, for example, builds a system that integrates diary data with data from different industries or fields to propose new services. For example, it integrates medical data with diary data to propose a health management service. The proposal unit can also analyze data from different industries or fields to predict user needs. For example, it can analyze trends in different industries to predict user needs. The proposal unit can also develop an algorithm for integrating data from different industries or fields. For example, it can integrate data from different industries and build a model for predicting needs. This makes it possible to propose new services by integrating diary data with data from different industries or fields.

[0071] The suggestion unit can share the user's need proposals with other users and connect users who have common needs. The suggestion unit, for example, builds a system that shares the user's need proposals with other users and connects users who have common needs. For example, it matches users who have the same hobbies. The suggestion unit can also develop an algorithm for connecting users who have common needs. For example, it can build a model for identifying and matching users who have common needs. The suggestion unit can also provide a platform for sharing need proposals. For example, it can provide a community function that allows users who have common needs to interact with each other. In this way, by sharing the user's need proposals with other users, users who have common needs can be connected with each other.

[0072] The suggestion unit can use the emotion estimation function to share need proposals based on the user's emotions with other users and promote empathy. The suggestion unit, for example, uses the emotion estimation function to share need proposals based on the user's emotions with other users and build a system to promote empathy. For example, needs with high emotion scores are shared. The suggestion unit can also make suggestions to promote empathy based on the emotion scores. For example, suggestions that show empathy for parts with high emotion scores are provided. The suggestion unit can also provide a platform for sharing need proposals with other users. For example, a community function for promoting empathy is provided, allowing users to share need proposals with each other. In this way, empathy can be promoted by sharing need proposals based on the user's emotions with other users.

[0073] The system can encrypt the user's diary data and introduce the latest encryption technology to strengthen data protection. For example, the system is constructed to encrypt the user's diary data and introduce the latest encryption technology to strengthen data protection. For example, AES-256 encryption technology is used. The system can also introduce technology for securely storing the encrypted data. For example, the encrypted data is securely stored in cloud storage. The system can also regularly update the encryption technology and introduce the latest security technology. For example, the encryption algorithm can be regularly updated to strengthen security. In this way, security can be ensured by encrypting the user's diary data and strengthening data protection.

[0074] The system can record detailed logs of data access and enable the detection and tracing of unauthorized access. For example, the system builds a system that records detailed logs of data access and enables the detection and tracing of unauthorized access. For example, the access logs are monitored in real time to detect abnormal access. The system can also introduce technology for tracing unauthorized access. For example, the access logs are analyzed to identify the source of the unauthorized access. The system can also introduce technology for safely storing the access logs. For example, the access logs are encrypted and safely stored. This makes it possible to record detailed logs of data access and enable the detection and tracing of unauthorized access, thereby strengthening security.

[0075] The system can use the emotion estimation function to provide security alerts based on the user's emotions in real time, thereby increasing the sense of security. For example, the system uses the emotion estimation function to build a system that provides security alerts based on the user's emotions in real time, thereby increasing the sense of security. For example, the system issues an alert when the user feels anxious. The system can also adjust the content of the security alert based on the emotion score. For example, if the emotion score is high, a more detailed alert is provided. The system can also introduce technology for analyzing emotions in real time and providing security alerts immediately. For example, the system can analyze emotions in real time and issue an alert immediately. This can increase the sense of security by providing security alerts based on the user's emotions in real time.

[0076] The system can strengthen data protection across different devices and platforms, allowing users to use their diaries safely from anywhere. For example, the system builds a system that strengthens data protection across different devices and platforms, allowing users to use their diaries safely from anywhere. For example, cloud storage is encrypted. The system can also introduce technology for securely synchronizing data across different devices and platforms. For example, data synchronization is encrypted. The system can also develop an algorithm for strengthening data protection across different devices and platforms. For example, a protocol is built for securely transferring data between devices. This strengthens data protection across different devices and platforms, allowing users to use their diaries safely from anywhere.

[0077] The system can periodically provide security-related information to enhance users' security awareness. For example, the system can be constructed to periodically provide security-related information to enhance users' security awareness. For example, security news and tips can be periodically distributed. The system can also provide a platform for providing security-related information. For example, a website that aggregates security-related information can be provided. The system can also provide an educational program to enhance users' security awareness. For example, an online course on security can be provided. In this way, security awareness can be improved by periodically providing security-related information to enhance users' security awareness.

[0078] The system can use the emotion estimation function to provide security feedback based on the user's emotions, thereby increasing the sense of security. For example, the system uses the emotion estimation function to build a system that provides security feedback based on the user's emotions and increases the sense of security. For example, when a user feels anxious, the system provides a message that gives the user a sense of security. The system can also adjust the content of the security feedback based on the emotion score. For example, if the emotion score is high, more detailed feedback is provided. The system can also introduce technology for analyzing emotions in real time and providing instantaneous security feedback. For example, the system can analyze emotions in real time and provide instantaneous feedback. In this way, the system can increase the sense of security by providing security feedback based on the user's emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The diary analysis unit can also monitor the user's health condition when analyzing the contents of the user's diary. For example, if the user writes in the diary that "I've been feeling tired lately," the diary analysis unit can evaluate the user's health condition based on that information and, if necessary, suggest that the user visit a medical institution. The diary analysis unit can also analyze the user's descriptions of diet and exercise and provide advice on maintaining a healthy lifestyle. Furthermore, the diary analysis unit can analyze the user's descriptions of sleep patterns and make suggestions for improving sleep quality. In this way, by analyzing the contents of the user's diary, it is possible to monitor the user's health condition and provide appropriate advice.

[0081] The diary analysis unit can analyze the contents of a user's diary and support the development of the user's creativity and ideas. For example, if a user writes an idea for a new project in their diary, the diary analysis unit can analyze the idea and provide related resources and reference materials. The diary analysis unit can also provide more specific advice by referring to the user's past ideas and projects and relating them to their current idea. Furthermore, the diary analysis unit can suggest brainstorming techniques and creative thinking methods to support the development of the user's ideas. In this way, analyzing the contents of a user's diary can support the development of the user's creativity and ideas.

[0082] The diary analysis unit can analyze the contents of a user's diary and support the user's learning and skill improvement. For example, if a user writes in their diary that they want to learn a new language, the diary analysis unit can suggest appropriate learning resources and materials based on that information. The diary analysis unit can also refer to the user's past learning experiences and provide advice related to their current learning goals. Furthermore, the diary analysis unit can monitor the user's learning progress and make suggestions to improve their learning methods and increase their motivation as needed. In this way, analyzing the contents of a user's diary can support learning and skill improvement.

[0083] The diary analysis unit can use the emotion estimation function to suggest relaxation methods based on the user's emotions. For example, if a user writes in their diary that they are "feeling stressed," the diary analysis unit can analyze that emotion and provide relaxation music or meditation guides. The diary analysis unit can also suggest relaxation activities based on the user's emotion score. Furthermore, the diary analysis unit can monitor changes in the user's emotions and provide long-term advice for stress management. This can support stress management by suggesting relaxation methods based on the user's emotions.

[0084] The diary analysis unit can analyze the contents of a user's diary and make suggestions to strengthen the user's social connections. For example, if a user writes in their diary that they want to "interact more with friends," the diary analysis unit can use that information to suggest events or activities to promote interaction with friends. The diary analysis unit can also refer to the user's past social activities and provide advice tailored to the user's current situation. Furthermore, the diary analysis unit can suggest online communities or group activities to strengthen the user's social connections. In this way, by analyzing the contents of a user's diary, suggestions to strengthen social connections can be made.

[0085] The diary analysis unit can use the emotion estimation function to provide feedback based on the user's emotions and support self-development. For example, if a user writes in their diary that they are "not confident," the diary analysis unit can analyze that emotion and provide feedback to increase self-esteem. The diary analysis unit can also suggest goal setting and action plans for self-development based on the user's emotion score. Furthermore, the diary analysis unit can monitor changes in the user's emotions and provide long-term support for self-development. This makes it possible to support self-development by providing feedback based on the user's emotions.

[0086] The diary analysis unit can analyze the contents of a user's diary and make suggestions to support the user's career development. For example, if a user writes in their diary that they are "considering a career change," the diary analysis unit can provide resources and advice that are useful for that career change based on that information. The diary analysis unit can also refer to the user's past work experience and skills to make suggestions related to their current career goals. Furthermore, the diary analysis unit can suggest networking opportunities and specialized training programs to support the user's career development. In this way, by analyzing the contents of a user's diary, suggestions to support career development can be made.

[0087] The diary analysis unit can use the emotion estimation function to provide mental health support based on the user's emotions. For example, if a user writes in their diary that they are "depressed," the diary analysis unit can analyze that emotion and suggest consulting a mental health professional. The diary analysis unit can also suggest resources and activities that will help improve the user's mental health based on the user's emotion score. Furthermore, the diary analysis unit can monitor changes in the user's emotions and provide long-term mental health support. This makes it possible to support mental health by providing mental health support based on the user's emotions.

[0088] The diary analysis unit can analyze the contents of a user's diary and suggest new activities based on the user's hobbies and interests. For example, if a user writes in their diary that they want to find a new hobby, the diary analysis unit can use that information to suggest hobbies and activities that match the user's interests. The diary analysis unit can also refer to the user's past hobbies and interests to make suggestions based on the user's current situation. Furthermore, the diary analysis unit can introduce related events and communities based on the user's hobbies and interests. In this way, analyzing the user's diary content can suggest new activities and broaden the user's hobbies and interests.

[0089] The diary analysis unit can use the emotion estimation function to suggest a personalized exercise plan based on the user's emotions. For example, if a user writes in their diary that they "feel like they're not getting enough exercise," the diary analysis unit can analyze that emotion and suggest an exercise plan that matches the user's emotional state. The diary analysis unit can also suggest the type and frequency of exercise to increase motivation based on the user's emotion score. Furthermore, the diary analysis unit can monitor changes in the user's emotions and adjust the exercise plan. This can support healthy lifestyle habits by suggesting a personalized exercise plan based on the user's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: When a user writes a diary, the diary analysis unit analyzes its contents. For example, it uses generation AI to analyze the user's writing and read their hobbies, preferences, personality, and psychological state. It can also use natural language processing technology to understand the meaning of the writing and analyze the user's emotions. It can also use text generation AI (e.g., LLM) to analyze the writing and estimate the user's emotions. It is also possible to use multimodal generation AI to analyze image and audio data. Step 2: The comment generation unit generates appropriate comments based on the content analyzed by the diary analysis unit. For example, if a user writes, "I failed at work today. I'm feeling really down," the unit can provide encouraging words such as, "Don't worry, everyone makes mistakes. I'm sure you'll do better next time." Also, if a user writes, "I started a new hobby today. It's so much fun," the unit can provide compliments such as, "It's great that you're finding a new hobby. Have fun." Step 3: The suggestion unit suggests potential needs of the user based on the comments generated by the comment generation unit. For example, if the user writes, "I feel like I haven't been getting enough exercise lately," the suggestion unit may make a suggestion such as, "Why don't you sign up for a membership at a nearby gym?". Also, if the user writes, "I want to read a new book," the suggestion unit may make a suggestion such as, "I recommend this book."

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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 AI 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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 AI 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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 AI 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 diary analysis unit that analyzes the contents of a diary written by a user; a comment generation unit that generates appropriate comments based on the content analyzed by the diary analysis unit; a suggestion unit that suggests potential needs of users based on the comments generated by the comment generation unit. A system characterized by:

2. The diary analysis unit The generative AI utilizes advanced natural language processing techniques to understand the subtle nuances and context of the user's writing.

2. The system of claim 1.

3. The diary analysis unit Analyzing the diaries of users from different cultures and languages ​​to gain a deeper understanding from a global perspective 2. The system of claim 1.

4. The comment generation unit Using an emotion estimation function, the comment is generated in real time according to the emotion of the user, and the comment responds immediately to changes in the emotion. The system of claim 1 .

5. The proposal unit Analyzing the user's diary data, discovering long-term trends and patterns, and predicting the potential needs based on those trends and patterns 2. The system of claim 1.

6. The system comprises: Using emotion estimation, security alerts are provided in real time based on the user's emotions, enhancing their sense of security.

2. The system of claim 1.

7. The diary analysis unit The contents of the user's diary are analyzed, and the transition of emotions is tracked over a long period of time, and emotional patterns are analyzed.

2. The system of claim 1.

8. The proposal unit Using an emotion estimation function, a need proposal based on the user's emotion is shared with the other users to promote empathy.

2. The system of claim 1.

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