system

Abstract

The system according to this embodiment aims to analyze user information and provide personalized mental care. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a recording unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides content based on the results of the analysis performed by the analysis unit. The recording unit records the user's actions based on the content provided by the provision unit.

Description

Technical Field

[0003]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized mental care based on user information has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user information and provide personalized mental care.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a recording unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides content based on the results of the analysis performed by the analysis unit. The recording unit records the user's actions based on the content provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user information and provide personalized mental care. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The mental care system according to an embodiment of the present invention is a system that utilizes generative AI to collect, analyze, and provide optimized content for users. This system comprehensively analyzes information obtained from daily activities via smartphones and personal computers, in addition to information pre-registered by the user, and provides content such as text, images, and videos optimized for each individual case, thereby realizing more personalized mental care. For example, the user registers their own information. For example, they answer a questionnaire and input their background, characteristics, and special health notes. Information obtained from daily activities via smartphones and personal computers is also collected. For example, this includes search and browsing history, SNS posts, and range of activity obtained by acquiring latitude and longitude. Next, the generative AI comprehensively analyzes this information. The generative AI identifies the cause of the user's worries and stress and generates content such as text, images, and videos optimized for each individual case. For example, if a user "consults about a worry," the generative AI provides specific methods and procedures, opinions from multiple perspectives, and relaxation content. Furthermore, the generative AI records and continuously analyzes the user's behavior. This makes it possible to continuously provide mental care that is tailored to the user's condition and needs. For example, if a user is feeling stressed, the generative AI will provide relaxing content to support their mental health. This system targets everyone who generally experiences stress or worries, including businessmen and businesswomen, students, parents, and the elderly. To address challenges such as time constraints, access limitations, privacy concerns, lack of effective care, and information overload, the system utilizes generative AI to provide personalized care. It also provides a system that can be used anytime, anywhere via smartphones and personal computers, creating a safe and secure environment while maintaining anonymity. This system enables effective mental care in a short amount of time, making it easily accessible even for busy people. Furthermore, it provides content based on reliable information, building a system that users can use with peace of mind.For example, based on data analyzed by the generative AI, it can provide users with advice on optimal relaxation methods and stress management. In this way, a new mental care system utilizing generative AI can provide personalized care tailored to individual needs, thereby alleviating worries and stress in modern society. This allows the mental care system to achieve personalized mental care by collecting, analyzing, providing, and recording user information.

[0029] The mental care system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a recording unit. The collection unit collects user information. The collection unit can collect user information, for example, in the form of a questionnaire. The collection unit can also collect information obtained from daily activities, for example, through a smartphone or personal computer. For example, the collection unit collects the user's search history, browsing history, SNS posts, and range of activity obtained by acquiring latitude and longitude. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses generative AI to identify the causes of the user's worries and stress. For example, the analysis unit analyzes the user's behavioral data and emotional data to identify the causes of stress. The provision unit provides content based on the results analyzed by the analysis unit. The provision unit uses generative AI to generate content such as text, images, and videos optimized for individual cases. For example, the provision unit provides content that helps the user relax and advice on stress management. The recording unit records the user's behavior based on the content provided by the provision unit. For example, the recording unit continuously analyzes the user's behavioral data and provides mental care according to the user's condition and needs. As a result, the mental care system according to the embodiment can realize personalized mental care by collecting, analyzing, providing, and recording user information. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, and recording unit may be performed using AI, for example, or without AI. For example, the collection unit can use AI to collect user information in the form of a questionnaire. The analysis unit can input the information collected by the collection unit into a generating AI and perform analysis using the generating AI. The provision unit can provide the user with content generated by the generating AI. The recording unit can use AI to record the user's behavior based on the content provided by the provision unit.

[0030] The data collection unit collects user information. For example, it can collect user information through questionnaires. Specifically, questionnaires are provided as online forms, and users respond using smartphones or personal computers. The questionnaires cover a wide range of topics, including the user's current mood, stress level, daily activities, sleep patterns, and eating habits. The data collection unit can also collect information obtained from daily activities via smartphones and personal computers. For example, it can collect the user's search history, browsing history, social media posts, and range of activity obtained through latitude and longitude data. This allows for an understanding of the user's interests, concerns, and daily behavioral patterns. Furthermore, the data collection unit can also collect data from wearable devices. For example, it can collect heart rate, steps, exercise levels, and sleep data to monitor the user's physical condition. This allows the data collection unit to comprehensively collect diverse data related to the user's mental health and provide it to the analysis unit. The collected data is stored on a secure cloud server and encrypted for privacy protection. The data collection unit adjusts the frequency and type of data collection based on user consent, and collects information while respecting user privacy.

[0031] The analysis unit analyzes the information collected by the data collection unit. The analysis unit uses generative AI to identify the causes of users' worries and stress. Specifically, the generative AI uses natural language processing technology to analyze the free-response sections of questionnaires and the content of social media posts to extract the user's emotions and the causes of stress. For example, if a user posts "I'm tired because I'm busy with work," the generative AI identifies "busy work" as the cause of stress. The generative AI also analyzes the user's behavioral data and emotional data to identify the causes of stress. For example, if a user frequently searches for information on a particular topic based on their search and browsing history, the generative AI determines that this topic is of interest to the user and may be a cause of stress. Furthermore, the generative AI analyzes data from wearable devices to evaluate the relationship between the user's physical condition and mental health. For example, it determines that an increased heart rate or decreased sleep quality are signs of stress. The analysis unit comprehensively analyzes this data to evaluate the user's mental health status. The analysis results are generated as personalized reports for each user and sent to the service delivery unit. The analysis department can also utilize historical data and statistical information to analyze user mental health trends and develop long-term care plans.

[0032] The service provider delivers content based on the results analyzed by the analysis department. The service provider uses generative AI to generate content such as text, images, and videos optimized for individual cases. Specifically, the generative AI generates relaxing music, meditation guides, and stress management advice based on the user's stressors and mental health status. For example, if a user is experiencing work-related stress, the generative AI provides videos of relaxation techniques and stretches that can be done during work breaks. If a user has sleep quality issues, the generative AI provides tips for better sleep and relaxing music. The service provider delivers this content to the user's smartphone or personal computer, making it accessible at any time. Furthermore, the service provider collects user feedback to evaluate the effectiveness of the content. For example, it collects user impressions and suggestions for improvement after using the provided content and provides this feedback to the generative AI. This allows the service provider to continuously improve the quality of the content and provide optimal mental care tailored to the user's needs. The service provider implements security measures in content delivery and feedback collection to protect user privacy.

[0033] The Records Unit records user behavior based on content provided by the Content Provider Unit. For example, the Records Unit continuously analyzes user behavior data to provide mental care tailored to the user's condition and needs. Specifically, the Records Unit records how much the user utilized the provided content, as well as their impressions and feedback after use. For instance, it records the amount of time a user listened to relaxation music or the number of times they followed a meditation guide, and evaluates its effectiveness. The Records Unit also analyzes user behavior data to monitor improvements in mental health. For example, it analyzes the user's heart rate and sleep data to evaluate changes in stress levels. Based on this data, the Records Unit continuously assesses the user's mental health status and provides feedback to the Content Provider Unit as needed. This enables the Content Provider Unit to continue providing optimal content tailored to the user's condition. Furthermore, the Records Unit implements security measures in data recording and analysis to protect user privacy. Recorded data is stored on a secure cloud server and used only with the user's consent. The Records Unit continuously monitors and provides feedback on data to support improvements in user mental health and maximize the overall system's effectiveness.

[0034] The data collection unit can collect user information in the form of a questionnaire. For example, the data collection unit can present a questionnaire to the user and collect information when the user answers it. For example, the data collection unit can collect information such as the user's background, characteristics, and health notes in the form of a questionnaire. For example, the data collection unit can store the user's responses in a database and provide it to the analysis unit. This allows for obtaining detailed information about the user by collecting information in the form of a questionnaire. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can automate the collection of information in the form of a questionnaire using AI.

[0035] The data collection unit can collect information obtained from daily activities through smartphones and personal computers. For example, the data collection unit can collect user search history, browsing history, social media posts, and range of activity obtained by acquiring latitude and longitude. For example, the data collection unit can collect user location information and activity data through smartphone applications. For example, the data collection unit can also collect browser history and application usage history from personal computers. By collecting information obtained from daily activities, a more detailed understanding of the user's situation can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from smartphones and personal computers into AI to automate data collection.

[0036] The analysis unit can identify the causes of users' worries and stress. For example, the analysis unit can analyze user behavior data and emotional data collected by the data collection unit to identify the causes of stress. The analysis unit can use generative AI to analyze users' behavior patterns and emotional changes. For example, the analysis unit can analyze users' search history and social media posts to identify the causes of stress. The analysis unit can also analyze users' location information and activity data to identify the causes of stress. By identifying the causes of users' worries and stress, appropriate mental care can be provided. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input data collected by the data collection unit into the generative AI, and the generative AI can analyze the data.

[0037] The service provider can generate content such as text, images, and videos optimized for individual cases. Using generative AI, the service provider generates optimal content tailored to the user's situation. For example, it can generate text, images, and videos that help users relax. It can also provide advice and guidance to help users manage stress. For instance, it can provide advice on relaxation methods and stress management based on the user's behavioral and emotional data. This allows the service provider to provide appropriate mental care by generating content optimized for individual cases. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can provide users with content generated by the generative AI.

[0038] The recording unit can continuously analyze user behavior data. For example, the recording unit can periodically collect user behavior data and provide it to the analysis unit. For example, the recording unit can continuously collect user location information and activity data and provide it to the analysis unit. For example, the recording unit can continuously collect user emotional data and provide it to the analysis unit. This allows for the provision of mental care tailored to the user's state and needs by continuously analyzing user behavior data. Some or all of the above-described processes in the recording unit may be performed using AI or not. For example, the recording unit can input user behavior data into AI to automate data collection and analysis.

[0039] The service provider can offer specific methods and procedures, multifaceted opinions, and relaxation content. Using generative AI, the service provider provides specific methods and procedures tailored to the user's situation. For example, the service provider can offer specific methods and procedures that help the user relax. Furthermore, the service provider can offer multifaceted opinions. For example, the service provider can offer multifaceted opinions based on expert opinions and user reviews. In addition, the service provider can offer relaxation content. For example, the service provider can offer relaxation content such as meditation guides and relaxation music. By providing specific methods and procedures, multifaceted opinions, and relaxation content, the service provider supports the user's mental well-being. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can provide users with specific methods and procedures, multifaceted opinions, and relaxation content generated by generative AI.

[0040] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit may prioritize using information collection methods that the user has frequently used in the past. For example, the data collection unit can analyze the user's past behavior patterns and suggest the optimal information collection method. For example, the data collection unit can select the optimal information collection method for a specific time period based on the user's past behavior history. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavior history into AI and have the AI ​​perform analysis to select the optimal information collection method.

[0041] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to areas of interest that the user is currently interested in. For example, the data collection unit can filter information appropriately based on the user's current living situation. For example, the data collection unit can also collect highly relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform analysis to filter information based on the user's living situation and areas of interest.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can collect local events and news based on the user's geographical location. For example, the data collection unit can also collect information on nearby relaxation spots and cafes based on the user's location. This allows for the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into AI and have AI perform analysis to prioritize the collection of highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant information. For example, the data collection unit can also collect information shared by the user's social media followers and friends. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform analysis to collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the information into the AI ​​and have the AI ​​perform an analysis to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to health-related information. For example, the analysis unit can apply a visually appealing analysis algorithm to entertainment-related information. For example, the analysis unit can apply a detailed analysis algorithm to learning-related information. By applying different analysis algorithms depending on the category of information, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of information into the AI ​​and have the AI ​​perform the analysis to apply different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for information that has been submitted earlier. The analysis unit may also adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input the submission date of the information into the AI ​​and have the AI ​​perform an analysis to determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform an analysis to adjust the order of analysis.

[0048] The content delivery unit can adjust the level of detail provided based on the importance of the content. For example, the delivery unit can provide detailed information for high-importance content, and simplified information for low-importance content. The delivery unit can also adjust the level of detail provided according to the importance of the content. This allows for efficient content delivery by adjusting the level of detail based on the importance of the content. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the importance of the content into the AI ​​and have the AI ​​perform an analysis to adjust the level of detail provided.

[0049] The content delivery unit can apply different delivery algorithms depending on the content category at the time of delivery. For example, the delivery unit can apply a specialized delivery algorithm to health-related content. For example, the delivery unit can apply a visually appealing delivery algorithm to entertainment-related content. For example, the delivery unit can apply a detailed delivery algorithm to learning-related content. By applying different delivery algorithms depending on the content category, it becomes possible to deliver more appropriate content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the content category into the AI ​​and have the AI ​​perform analysis to apply different delivery algorithms.

[0050] The content delivery unit can determine the priority of content delivery based on the submission date. For example, the delivery unit may prioritize the delivery of the most recent content. For example, the delivery unit may lower the priority of older content. The delivery unit may also adjust the order of delivery based on the submission date. This enables efficient content delivery by determining the priority of content delivery based on the submission date. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the submission dates of the content into the AI ​​and have the AI ​​perform analysis to determine the priority of delivery.

[0051] The content delivery unit can adjust the order of delivery based on the relevance of the content. For example, the delivery unit can prioritize the delivery of highly relevant content. For example, the delivery unit can postpone the delivery of less relevant content. The delivery unit can also adjust the order of delivery based on the relevance of the content. This allows for efficient content delivery by adjusting the order of delivery based on the relevance of the content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the relevance of the content into the AI ​​and have the AI ​​perform analysis to adjust the order of delivery.

[0052] The recording unit can analyze the user's past behavioral data to select the optimal recording method during recording. For example, the recording unit may prioritize using recording methods previously used by the user. For example, the recording unit can analyze the user's past behavioral data and suggest the optimal recording method. For example, the recording unit can select the optimal recording method for a specific time period based on the user's past behavioral data. In this way, the optimal recording method can be selected by analyzing the user's past behavioral data. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past behavioral data into AI and have the AI ​​perform the analysis to select the optimal recording method.

[0053] The recording unit can customize the recording method based on the user's current living situation at the time of recording. For example, if the user is busy, the recording unit can provide a simplified recording method. For example, if the user is relaxed, the recording unit can provide a detailed recording method. The recording unit can also customize the recording method according to the user's living situation. This allows for more appropriate recording by customizing the recording method based on the user's living situation. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's living situation into the AI ​​and have the AI ​​perform analysis to customize the recording method.

[0054] The recording unit can select the optimal recording method while considering the user's geographical location information. For example, if the user is in a specific location, the recording unit will prioritize recording data related to that location. For example, the recording unit can select the optimal recording method based on the user's geographical location information. For example, the recording unit can also record highly relevant data based on the user's location information. This allows the optimal recording method to be selected by considering the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location information into AI and have the AI ​​perform analysis to select the optimal recording method.

[0055] The recording unit can analyze the user's social media activity and suggest recording methods during recording. For example, the recording unit can analyze the content of the user's social media posts and record relevant data. For example, the recording unit can analyze the user's social media activity patterns and suggest the optimal recording method. For example, the recording unit can also record information shared by the user's social media followers and friends. This allows the optimal recording method to be suggested by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity into AI and have the AI ​​perform analysis to suggest recording methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The mental care system can also include a biometric data analysis unit that collects and analyzes the user's biometric data. This unit can collect and analyze biometric data such as the user's heart rate, blood pressure, and body temperature. For example, it can analyze fluctuations in heart rate to estimate the user's stress level. It can also analyze changes in blood pressure to understand the user's health status. Furthermore, it can analyze fluctuations in body temperature to detect changes in the user's physical condition. This allows for a detailed understanding of the user's health status and the provision of appropriate mental care by utilizing biometric data.

[0058] The mental care system can also include a sleep analysis unit that collects and analyzes the user's sleep data. This unit can analyze the user's sleep patterns and quality, and provide information about the user's mental health. For example, it can analyze the user's sleep duration and the proportion of deep sleep to evaluate sleep quality. It can also analyze changes in movement and breathing during sleep to estimate the user's stress level. Furthermore, the sleep analysis unit can provide advice to help the user create a relaxing sleep environment. In this way, by utilizing sleep data, the system can comprehensively support the user's mental health.

[0059] The mental care system can also include a dietary analysis unit that collects and analyzes the user's dietary data. This unit can analyze the user's diet and meal timing, providing information related to the user's mental health. For example, it can analyze the user's diet and evaluate nutritional balance. It can also analyze meal timing to understand the user's lifestyle rhythm. Furthermore, the dietary analysis unit can provide advice to create a relaxing dining environment for the user. In this way, by utilizing dietary data, the system can comprehensively support the user's mental health.

[0060] The mental care system can also include an exercise analysis unit that collects and analyzes the user's exercise data. The exercise analysis unit can analyze the user's exercise volume and type, and provide information about the user's mental health. For example, it can analyze the user's exercise volume to assess insufficient or excessive exercise. It can also analyze the type of exercise to estimate the user's stress level. Furthermore, the exercise analysis unit can provide advice on exercise methods that help the user relax. In this way, by utilizing exercise data, the system can comprehensively support the user's mental health.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects user information. The data collection unit can collect user information, for example, through questionnaires. It can also collect information obtained from daily activities via smartphones and personal computers. Specifically, it collects user search history, browsing history, social media posts, and range of activity obtained by acquiring latitude and longitude coordinates. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses generative AI to identify the causes of the user's worries and stress. For example, it analyzes the user's behavioral data and emotional data to identify the causes of stress. Step 3: The delivery unit provides content based on the results analyzed by the analysis unit. The delivery unit uses a generation AI to generate content such as text, images, and videos optimized for individual cases. For example, it may provide content that helps users relax or advice on stress management. Step 4: The recording unit records user behavior based on the content provided by the delivery unit. The recording unit, for example, continuously analyzes user behavior data and provides mental care tailored to the user's state and needs.

[0063] (Example of form 2) The mental care system according to an embodiment of the present invention is a system that utilizes generative AI to collect, analyze, and provide optimized content for users. This system comprehensively analyzes information obtained from daily activities via smartphones and personal computers, in addition to information pre-registered by the user, and provides content such as text, images, and videos optimized for each individual case, thereby realizing more personalized mental care. For example, the user registers their own information. For example, they answer a questionnaire and input their background, characteristics, and special health notes. Information obtained from daily activities via smartphones and personal computers is also collected. For example, this includes search and browsing history, SNS posts, and range of activity obtained by acquiring latitude and longitude. Next, the generative AI comprehensively analyzes this information. The generative AI identifies the cause of the user's worries and stress and generates content such as text, images, and videos optimized for each individual case. For example, if a user "consults about a worry," the generative AI provides specific methods and procedures, opinions from multiple perspectives, and relaxation content. Furthermore, the generative AI records and continuously analyzes the user's behavior. This makes it possible to continuously provide mental care that is tailored to the user's condition and needs. For example, if a user is feeling stressed, the generative AI will provide relaxing content to support their mental health. This system targets everyone who generally experiences stress or worries, including businessmen and businesswomen, students, parents, and the elderly. To address challenges such as time constraints, access limitations, privacy concerns, lack of effective care, and information overload, the system utilizes generative AI to provide personalized care. It also provides a system that can be used anytime, anywhere via smartphones and personal computers, creating a safe and secure environment while maintaining anonymity. This system enables effective mental care in a short amount of time, making it easily accessible even for busy people. Furthermore, it provides content based on reliable information, building a system that users can use with peace of mind.For example, based on data analyzed by the generative AI, it can provide users with advice on optimal relaxation methods and stress management. In this way, a new mental care system utilizing generative AI can provide personalized care tailored to individual needs, thereby alleviating worries and stress in modern society. This allows the mental care system to achieve personalized mental care by collecting, analyzing, providing, and recording user information.

[0064] The mental care system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a recording unit. The collection unit collects user information. The collection unit can collect user information, for example, in the form of a questionnaire. The collection unit can also collect information obtained from daily activities, for example, through a smartphone or personal computer. For example, the collection unit collects the user's search history, browsing history, SNS posts, and range of activity obtained by acquiring latitude and longitude. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses generative AI to identify the causes of the user's worries and stress. For example, the analysis unit analyzes the user's behavioral data and emotional data to identify the causes of stress. The provision unit provides content based on the results analyzed by the analysis unit. The provision unit uses generative AI to generate content such as text, images, and videos optimized for individual cases. For example, the provision unit provides content that helps the user relax and advice on stress management. The recording unit records the user's behavior based on the content provided by the provision unit. For example, the recording unit continuously analyzes the user's behavioral data and provides mental care according to the user's condition and needs. As a result, the mental care system according to the embodiment can realize personalized mental care by collecting, analyzing, providing, and recording user information. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, and recording unit may be performed using AI, for example, or without AI. For example, the collection unit can use AI to collect user information in the form of a questionnaire. The analysis unit can input the information collected by the collection unit into a generating AI and perform analysis using the generating AI. The provision unit can provide the user with content generated by the generating AI. The recording unit can use AI to record the user's behavior based on the content provided by the provision unit.

[0065] The data collection unit collects user information. For example, it can collect user information through questionnaires. Specifically, questionnaires are provided as online forms, and users respond using smartphones or personal computers. The questionnaires cover a wide range of topics, including the user's current mood, stress level, daily activities, sleep patterns, and eating habits. The data collection unit can also collect information obtained from daily activities via smartphones and personal computers. For example, it can collect the user's search history, browsing history, social media posts, and range of activity obtained through latitude and longitude data. This allows for an understanding of the user's interests, concerns, and daily behavioral patterns. Furthermore, the data collection unit can also collect data from wearable devices. For example, it can collect heart rate, steps, exercise levels, and sleep data to monitor the user's physical condition. This allows the data collection unit to comprehensively collect diverse data related to the user's mental health and provide it to the analysis unit. The collected data is stored on a secure cloud server and encrypted for privacy protection. The data collection unit adjusts the frequency and type of data collection based on user consent, and collects information while respecting user privacy.

[0066] The analysis unit analyzes the information collected by the data collection unit. The analysis unit uses generative AI to identify the causes of users' worries and stress. Specifically, the generative AI uses natural language processing technology to analyze the free-response sections of questionnaires and the content of social media posts to extract the user's emotions and the causes of stress. For example, if a user posts "I'm tired because I'm busy with work," the generative AI identifies "busy work" as the cause of stress. The generative AI also analyzes the user's behavioral data and emotional data to identify the causes of stress. For example, if a user frequently searches for information on a particular topic based on their search and browsing history, the generative AI determines that this topic is of interest to the user and may be a cause of stress. Furthermore, the generative AI analyzes data from wearable devices to evaluate the relationship between the user's physical condition and mental health. For example, it determines that an increased heart rate or decreased sleep quality are signs of stress. The analysis unit comprehensively analyzes this data to evaluate the user's mental health status. The analysis results are generated as personalized reports for each user and sent to the service delivery unit. The analysis department can also utilize historical data and statistical information to analyze user mental health trends and develop long-term care plans.

[0067] The service provider delivers content based on the results analyzed by the analysis department. The service provider uses generative AI to generate content such as text, images, and videos optimized for individual cases. Specifically, the generative AI generates relaxing music, meditation guides, and stress management advice based on the user's stressors and mental health status. For example, if a user is experiencing work-related stress, the generative AI provides videos of relaxation techniques and stretches that can be done during work breaks. If a user has sleep quality issues, the generative AI provides tips for better sleep and relaxing music. The service provider delivers this content to the user's smartphone or personal computer, making it accessible at any time. Furthermore, the service provider collects user feedback to evaluate the effectiveness of the content. For example, it collects user impressions and suggestions for improvement after using the provided content and provides this feedback to the generative AI. This allows the service provider to continuously improve the quality of the content and provide optimal mental care tailored to the user's needs. The service provider implements security measures in content delivery and feedback collection to protect user privacy.

[0068] The Records Unit records user behavior based on content provided by the Content Provider Unit. For example, the Records Unit continuously analyzes user behavior data to provide mental care tailored to the user's condition and needs. Specifically, the Records Unit records how much the user utilized the provided content, as well as their impressions and feedback after use. For instance, it records the amount of time a user listened to relaxation music or the number of times they followed a meditation guide, and evaluates its effectiveness. The Records Unit also analyzes user behavior data to monitor improvements in mental health. For example, it analyzes the user's heart rate and sleep data to evaluate changes in stress levels. Based on this data, the Records Unit continuously assesses the user's mental health status and provides feedback to the Content Provider Unit as needed. This enables the Content Provider Unit to continue providing optimal content tailored to the user's condition. Furthermore, the Records Unit implements security measures in data recording and analysis to protect user privacy. Recorded data is stored on a secure cloud server and used only with the user's consent. The Records Unit continuously monitors and provides feedback on data to support improvements in user mental health and maximize the overall system's effectiveness.

[0069] The data collection unit can collect user information in the form of a questionnaire. For example, the data collection unit can present a questionnaire to the user and collect information when the user answers it. For example, the data collection unit can collect information such as the user's background, characteristics, and health notes in the form of a questionnaire. For example, the data collection unit can store the user's responses in a database and provide it to the analysis unit. This allows for obtaining detailed information about the user by collecting information in the form of a questionnaire. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can automate the collection of information in the form of a questionnaire using AI.

[0070] The data collection unit can collect information obtained from daily activities through smartphones and personal computers. For example, the data collection unit can collect user search history, browsing history, social media posts, and range of activity obtained by acquiring latitude and longitude. For example, the data collection unit can collect user location information and activity data through smartphone applications. For example, the data collection unit can also collect browser history and application usage history from personal computers. By collecting information obtained from daily activities, a more detailed understanding of the user's situation can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from smartphones and personal computers into AI to automate data collection.

[0071] The analysis unit can identify the causes of users' worries and stress. For example, the analysis unit can analyze user behavior data and emotional data collected by the data collection unit to identify the causes of stress. The analysis unit can use generative AI to analyze users' behavior patterns and emotional changes. For example, the analysis unit can analyze users' search history and social media posts to identify the causes of stress. The analysis unit can also analyze users' location information and activity data to identify the causes of stress. By identifying the causes of users' worries and stress, appropriate mental care can be provided. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input data collected by the data collection unit into the generative AI, and the generative AI can analyze the data.

[0072] The service provider can generate content such as text, images, and videos optimized for individual cases. Using generative AI, the service provider generates optimal content tailored to the user's situation. For example, it can generate text, images, and videos that help users relax. It can also provide advice and guidance to help users manage stress. For instance, it can provide advice on relaxation methods and stress management based on the user's behavioral and emotional data. This allows the service provider to provide appropriate mental care by generating content optimized for individual cases. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can provide users with content generated by the generative AI.

[0073] The recording unit can continuously analyze user behavior data. For example, the recording unit can periodically collect user behavior data and provide it to the analysis unit. For example, the recording unit can continuously collect user location information and activity data and provide it to the analysis unit. For example, the recording unit can continuously collect user emotional data and provide it to the analysis unit. This allows for the provision of mental care tailored to the user's state and needs by continuously analyzing user behavior data. Some or all of the above-described processes in the recording unit may be performed using AI or not. For example, the recording unit can input user behavior data into AI to automate data collection and analysis.

[0074] The service provider can offer specific methods and procedures, multifaceted opinions, and relaxation content. Using generative AI, the service provider provides specific methods and procedures tailored to the user's situation. For example, the service provider can offer specific methods and procedures that help the user relax. Furthermore, the service provider can offer multifaceted opinions. For example, the service provider can offer multifaceted opinions based on expert opinions and user reviews. In addition, the service provider can offer relaxation content. For example, the service provider can offer relaxation content such as meditation guides and relaxation music. By providing specific methods and procedures, multifaceted opinions, and relaxation content, the service provider supports the user's mental well-being. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can provide users with specific methods and procedures, multifaceted opinions, and relaxation content generated by generative AI.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect information during times when the user is relaxed. For example, if the user is busy, the data collection unit can collect information during times when the user is free. For example, if the user is relaxed, the data collection unit can also collect information during times when the user's emotions are stable. By adjusting the timing of information collection based on the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit may prioritize using information collection methods that the user has frequently used in the past. For example, the data collection unit can analyze the user's past behavior patterns and suggest the optimal information collection method. For example, the data collection unit can select the optimal information collection method for a specific time period based on the user's past behavior history. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavior history into AI and have the AI ​​perform analysis to select the optimal information collection method.

[0077] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to areas of interest that the user is currently interested in. For example, the data collection unit can filter information appropriately based on the user's current living situation. For example, the data collection unit can also collect highly relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform analysis to filter information based on the user's living situation and areas of interest.

[0078] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information that helps them relax. For example, if the user is excited, the data collection unit may prioritize collecting information that interests them. For example, if the user is calm, the data collection unit may prioritize collecting information that is helpful for learning or self-improvement. By prioritizing information based on the user's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can collect local events and news based on the user's geographical location. For example, the data collection unit can also collect information on nearby relaxation spots and cafes based on the user's location. This allows for the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into AI and have AI perform analysis to prioritize the collection of highly relevant information.

[0080] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant information. For example, the data collection unit can also collect information shared by the user's social media followers and friends. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform analysis to collect relevant information.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will use a simple and easy-to-understand presentation. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is excited, the analysis unit can also use a visually appealing presentation. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the information into the AI ​​and have the AI ​​perform an analysis to adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to health-related information. For example, the analysis unit can apply a visually appealing analysis algorithm to entertainment-related information. For example, the analysis unit can apply a detailed analysis algorithm to learning-related information. By applying different analysis algorithms depending on the category of information, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of information into the AI ​​and have the AI ​​perform the analysis to apply different analysis algorithms.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, for example, the analysis unit can perform a detailed analysis. If the user is excited, for example, the analysis unit can also perform a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using the generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for information that has been submitted earlier. The analysis unit may also adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input the submission date of the information into the AI ​​and have the AI ​​perform an analysis to determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform an analysis to adjust the order of analysis.

[0087] The service provider can estimate the user's emotions and adjust the presentation of the content based on the estimated emotions. For example, if the user is stressed, the service provider can use a relaxing presentation. For example, if the user is relaxed, the service provider can use a presentation that includes detailed information. For example, if the user is excited, the service provider can also use a visually appealing presentation. By adjusting the presentation of content based on the user's emotions, more appropriate content can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0088] The content delivery unit can adjust the level of detail provided based on the importance of the content. For example, the delivery unit can provide detailed information for high-importance content, and simplified information for low-importance content. The delivery unit can also adjust the level of detail provided according to the importance of the content. This allows for efficient content delivery by adjusting the level of detail based on the importance of the content. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the importance of the content into the AI ​​and have the AI ​​perform an analysis to adjust the level of detail provided.

[0089] The content delivery unit can apply different delivery algorithms depending on the content category at the time of delivery. For example, the delivery unit can apply a specialized delivery algorithm to health-related content. For example, the delivery unit can apply a visually appealing delivery algorithm to entertainment-related content. For example, the delivery unit can apply a detailed delivery algorithm to learning-related content. By applying different delivery algorithms depending on the content category, it becomes possible to deliver more appropriate content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the content category into the AI ​​and have the AI ​​perform analysis to apply different delivery algorithms.

[0090] The service provider can estimate the user's emotions and adjust the length of the content it provides based on those emotions. For example, if the user is in a hurry, the service provider can provide short, concise content. If the user is relaxed, the service provider can provide detailed content. If the user is excited, the service provider can provide visually stimulating content. By adjusting the length of the content based on the user's emotions, more appropriate content can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0091] The content delivery unit can determine the priority of content delivery based on the submission date. For example, the delivery unit may prioritize the delivery of the most recent content. For example, the delivery unit may lower the priority of older content. The delivery unit may also adjust the order of delivery based on the submission date. This enables efficient content delivery by determining the priority of content delivery based on the submission date. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the submission dates of the content into the AI ​​and have the AI ​​perform analysis to determine the priority of delivery.

[0092] The content delivery unit can adjust the order of delivery based on the relevance of the content. For example, the delivery unit can prioritize the delivery of highly relevant content. For example, the delivery unit can postpone the delivery of less relevant content. The delivery unit can also adjust the order of delivery based on the relevance of the content. This allows for efficient content delivery by adjusting the order of delivery based on the relevance of the content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the relevance of the content into the AI ​​and have the AI ​​perform analysis to adjust the order of delivery.

[0093] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit can use a simplified recording method. For example, if the user is relaxed, the recording unit can use a detailed recording method. For example, if the user is excited, the recording unit can also use a visually appealing recording method. By adjusting the recording method based on the user's emotions, more appropriate recording becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The recording unit can analyze the user's past behavioral data to select the optimal recording method during recording. For example, the recording unit may prioritize using recording methods previously used by the user. For example, the recording unit can analyze the user's past behavioral data and suggest the optimal recording method. For example, the recording unit can select the optimal recording method for a specific time period based on the user's past behavioral data. In this way, the optimal recording method can be selected by analyzing the user's past behavioral data. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past behavioral data into AI and have the AI ​​perform the analysis to select the optimal recording method.

[0095] The recording unit can customize the recording method based on the user's current living situation at the time of recording. For example, if the user is busy, the recording unit can provide a simplified recording method. For example, if the user is relaxed, the recording unit can provide a detailed recording method. The recording unit can also customize the recording method according to the user's living situation. This allows for more appropriate recording by customizing the recording method based on the user's living situation. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's living situation into the AI ​​and have the AI ​​perform analysis to customize the recording method.

[0096] The recording unit can estimate the user's emotions and determine the priority of recordings based on the estimated emotions. For example, if the user is stressed, the recording unit will prioritize recording data related to stress. For example, if the user is relaxed, the recording unit can prioritize recording data related to relaxation. For example, if the user is excited, the recording unit can prioritize recording data related to excitement. This allows for more appropriate recordings by prioritizing recordings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The recording unit can select the optimal recording method while considering the user's geographical location information. For example, if the user is in a specific location, the recording unit will prioritize recording data related to that location. For example, the recording unit can select the optimal recording method based on the user's geographical location information. For example, the recording unit can also record highly relevant data based on the user's location information. This allows the optimal recording method to be selected by considering the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location information into AI and have the AI ​​perform analysis to select the optimal recording method.

[0098] The recording unit can analyze the user's social media activity and suggest recording methods during recording. For example, the recording unit can analyze the content of the user's social media posts and record relevant data. For example, the recording unit can analyze the user's social media activity patterns and suggest the optimal recording method. For example, the recording unit can also record information shared by the user's social media followers and friends. This allows the optimal recording method to be suggested by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity into AI and have the AI ​​perform analysis to suggest recording methods.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The mental care system can also include a voice analysis unit that collects and analyzes the user's voice data. The voice analysis unit can analyze the content, tone, speed, and rhythm of the user's speech to estimate the user's emotions and stress levels. For example, it can extract specific keywords from the user's speech to identify the cause of stress. It can also analyze changes in voice tone and speed to detect changes in the user's emotions. Furthermore, the voice analysis unit can determine whether the user is relaxed and provide information to deliver appropriate content. This allows for a more detailed understanding of the user's state and the provision of appropriate mental care by utilizing voice data.

[0101] The mental care system can also include a biometric data analysis unit that collects and analyzes the user's biometric data. This unit can collect and analyze biometric data such as the user's heart rate, blood pressure, and body temperature. For example, it can analyze fluctuations in heart rate to estimate the user's stress level. It can also analyze changes in blood pressure to understand the user's health status. Furthermore, it can analyze fluctuations in body temperature to detect changes in the user's physical condition. This allows for a detailed understanding of the user's health status and the provision of appropriate mental care by utilizing biometric data.

[0102] The mental care system can also include a sleep analysis unit that collects and analyzes the user's sleep data. This unit can analyze the user's sleep patterns and quality, and provide information about the user's mental health. For example, it can analyze the user's sleep duration and the proportion of deep sleep to evaluate sleep quality. It can also analyze changes in movement and breathing during sleep to estimate the user's stress level. Furthermore, the sleep analysis unit can provide advice to help the user create a relaxing sleep environment. In this way, by utilizing sleep data, the system can comprehensively support the user's mental health.

[0103] The mental care system can also include a dietary analysis unit that collects and analyzes the user's dietary data. This unit can analyze the user's diet and meal timing, providing information related to the user's mental health. For example, it can analyze the user's diet and evaluate nutritional balance. It can also analyze meal timing to understand the user's lifestyle rhythm. Furthermore, the dietary analysis unit can provide advice to create a relaxing dining environment for the user. In this way, by utilizing dietary data, the system can comprehensively support the user's mental health.

[0104] The mental care system can also include an exercise analysis unit that collects and analyzes the user's exercise data. The exercise analysis unit can analyze the user's exercise volume and type, and provide information about the user's mental health. For example, it can analyze the user's exercise volume to assess insufficient or excessive exercise. It can also analyze the type of exercise to estimate the user's stress level. Furthermore, the exercise analysis unit can provide advice on exercise methods that help the user relax. In this way, by utilizing exercise data, the system can comprehensively support the user's mental health.

[0105] The mental care system can further estimate the user's emotions and adjust the type of content provided based on those emotions. For example, if the user is stressed, it can provide relaxing music or meditation guides. If the user is relaxed, it can provide content that helps with learning or self-improvement. Furthermore, if the user is agitated, it can provide highly entertaining content. In this way, by adjusting the type of content provided based on the user's emotions, more appropriate mental care can be delivered.

[0106] The mental care system can further estimate the user's emotions and adjust the timing of content delivery based on those emotions. For example, if a user is stressed, it can immediately provide relaxing content. If the user is relaxed, it can adjust the timing of content that helps with learning and self-improvement. Furthermore, if the user is agitated, it can adjust the timing of entertainment content. By adjusting the timing of content delivery based on the user's emotions, it can provide more appropriate mental care.

[0107] The mental care system can further estimate the user's emotions and adjust the format of the content it provides based on those emotions. For example, if the user is stressed, it can provide visually relaxing images or videos. If the user is relaxed, it can provide detailed text or audio guides. Furthermore, if the user is agitated, it can provide interactive content. By adjusting the format of the content provided based on the user's emotions, it can provide more appropriate mental care.

[0108] The mental care system can further estimate the user's emotions and adjust the amount of content provided based on those emotions. For example, if the user is stressed, it can provide short, concise content. If the user is relaxed, it can provide more detailed content. Furthermore, if the user is agitated, it can provide visually stimulating content. By adjusting the amount of content provided based on the user's emotions, it can provide more appropriate mental care.

[0109] The mental care system can further estimate the user's emotions and adjust the order of content provided based on those emotions. For example, if the user is stressed, relaxing content can be provided first. If the user is relaxed, content that helps with learning or self-improvement can be provided later. Furthermore, if the user is excited, highly entertaining content can be provided first. By adjusting the order of content provided based on the user's emotions, more appropriate mental care can be delivered.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects user information. The data collection unit can collect user information, for example, through questionnaires. It can also collect information obtained from daily activities via smartphones and personal computers. Specifically, it collects user search history, browsing history, social media posts, and range of activity obtained by acquiring latitude and longitude coordinates. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses generative AI to identify the causes of the user's worries and stress. For example, it analyzes the user's behavioral data and emotional data to identify the causes of stress. Step 3: The delivery unit provides content based on the results analyzed by the analysis unit. The delivery unit uses a generation AI to generate content such as text, images, and videos optimized for individual cases. For example, it may provide content that helps users relax or advice on stress management. Step 4: The recording unit records user behavior based on the content provided by the delivery unit. The recording unit, for example, continuously analyzes user behavior data and provides mental care tailored to the user's state and needs.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The provision unit provides the user with content generated by the specific processing unit 290 of the data processing unit 12 through the display 40A and speaker 40B of the smart device 14. The recording unit records the user's actions based on the content provided by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and recording unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The provision unit provides, for example, content generated by the specific processing unit 290 of the data processing unit 12 to the user through the speaker 240 of the smart glasses 214. The recording unit records the user's actions based on the content provided by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and recording unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The provision unit provides to the user, for example, content generated by the specific processing unit 290 of the data processing unit 12 through the display 343 and speaker 240 of the headset terminal 314. The recording unit records the user's actions based on the content provided by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and recording unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The provision unit provides the user with content generated by the specific processing unit 290 of the data processing unit 12 through the speaker 240 and display device of the robot 414. The recording unit records the user's actions based on the content provided by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A content provision unit provides content based on the results of analysis performed by the aforementioned analysis unit, The system includes a recording unit that records user behavior based on the content provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user information in the form of a survey. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect information obtained from daily activities through smartphones and personal computers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Identifying the root causes of users' worries and stress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Generates content such as text, images, and videos optimized for individual cases. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recording unit is Continuously analyze user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We provide specific methods and procedures, opinions from various perspectives, and relaxing content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing content, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing content, different delivery algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing content, we will prioritize its delivery based on the submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When delivering content, the order of delivery will be adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recording unit is The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recording unit is During recording, the system analyzes the user's past behavioral data to select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recording unit is During recording, the recording method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recording unit is The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recording unit is During recording, the optimal recording method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recording unit is During recording, we analyze the user's social media activity and suggest recording methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A content provision unit provides content based on the results of analysis performed by the aforementioned analysis unit, The system includes a recording unit that records user behavior based on the content provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user information in the form of a survey. The system according to feature 1.

3. The aforementioned collection unit is Collect information obtained from daily activities through smartphones and personal computers. The system according to feature 1.

4. The aforementioned analysis unit, Identifying the root causes of users' worries and stress. The system according to feature 1.

5. The aforementioned supply unit is, Generates content such as text, images, and videos optimized for individual cases. The system according to feature 1.

6. The recording unit is, Continuously analyze user behavior data. The system according to feature 1.

7. The aforementioned supply unit is, We provide specific methods and procedures, opinions from various perspectives, and relaxing content. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

Images