system

Abstract

The system according to this embodiment aims to suggest and provide the optimal menu based on the user's hobbies and preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects data related to the user's hobbies and thoughts. The analysis unit analyzes the data collected by the collection unit and creates a menu that is optimal for the user. The proposal unit makes specific suggestions based on the menu created by the analysis unit. The provision unit provides the menu proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized proposals based on the user's hobbies and thoughts have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to propose and provide an optimal menu based on the user's hobbies and thoughts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data related to the user's hobbies and preferences. The analysis unit analyzes the data collected by the data collection unit and creates a menu that is optimal for the user. The proposal unit makes specific suggestions based on the menu created by the analysis unit. The provision unit provides the menu proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest and provide the optimal menu based on the user's preferences and tastes. [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 controls communication among a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 dementia prevention system according to an embodiment of the present invention is a mobile application that utilizes AI to provide personalized dementia prevention menus. This dementia prevention system prevents dementia by collecting, analyzing, suggesting, and providing data related to the user's hobbies and thoughts. Specifically, the collection unit collects data related to the user's hobbies and thoughts, the analysis unit analyzes the collected data and creates an optimal menu for the user, the suggestion unit makes specific suggestions based on the analysis results, and the provision unit provides the suggested menu to the user. This service prevents dementia in the following three ways: 1. Prevention through social connections, 2. Prevention through suggestions such as games and travel, and 3. Prevention through maintaining health. For example, the collection unit collects the user's search history and topics of interest. Next, the analysis unit analyzes the collected data and creates an optimal menu for the user. For example, it suggests an appropriate menu based on the user's interests. The suggestion unit makes specific suggestions based on the analysis results. For example, it suggests appropriate games or travel plans based on the user's interests. Finally, the provision unit provides the suggested menu to the user. For example, it suggests appropriate methods for maintaining health based on the user's interests. Thus, AI-powered dementia prevention services provide personalized menus based on the user's hobbies and preferences, preventing dementia in three ways: through social connection, game and travel suggestions, and health maintenance. This allows the dementia prevention system to provide personalized dementia prevention menus based on the user's hobbies and preferences.

[0029] The dementia prevention system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a provision unit. The data collection unit collects data related to the user's hobbies and thoughts. For example, the data collection unit collects the user's search history and topics of interest. For example, the data collection unit can analyze the user's search history and identify topics of interest. The data collection unit can also collect data such as events the user has participated in and articles the user has viewed. The analysis unit analyzes the data collected by the data collection unit and creates an optimal menu for the user. For example, the analysis unit analyzes the user's hobbies and thoughts based on the collected data and creates an optimal menu. For example, the analysis unit can suggest an appropriate menu based on topics the user is interested in. The analysis unit can also analyze the user's past behavioral history and create an optimal menu. The suggestion unit makes specific suggestions based on the menu created by the analysis unit. For example, the suggestion unit can suggest appropriate games or travel plans based on topics the user is interested in. For example, the suggestion unit can suggest appropriate methods for maintaining health based on topics the user is interested in. Furthermore, the suggestion unit can also suggest appropriate club activities and events based on the user's hobbies and preferences. The provision unit provides the menus suggested by the suggestion unit. The provision unit can, for example, provide appropriate methods for maintaining health based on topics the user is interested in. The provision unit can, for example, provide appropriate games and travel plans based on topics the user is interested in. Furthermore, the provision unit can also provide appropriate club activities and events based on the user's hobbies and preferences. As a result, the dementia prevention system according to the embodiment can provide a personalized dementia prevention menu based on the user's hobbies and preferences.

[0030] The data collection unit collects data related to users' hobbies and interests. Specifically, it collects users' search history and topics of interest. For example, it analyzes keywords users have searched for on the internet and the history of web pages they have viewed to identify topics that users are interested in. It can also collect data on events users have attended and articles they have viewed. This includes information on webinars and seminars users have attended online, or events they have actually visited. Furthermore, posts that users have "liked" or shared on social media, and articles they have commented on, are also collected. This allows the data collection unit to understand users' interests and preferences from multiple perspectives. The collected data is stored on a cloud server and made accessible to the analysis unit. The frequency and accuracy of data collection can be adjusted according to user settings and system requirements, allowing for flexible responses to specific situations and conditions. For example, if a user starts a new hobby, information about it can be quickly collected, improving the overall personalization of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit to create the optimal menu for the user. Specifically, it analyzes the user's hobbies and preferences based on the collected data to create the optimal menu. For example, it can suggest appropriate menus based on topics the user is interested in. The analysis unit uses AI to analyze data and gain a deep understanding of the user's interests and preferences. For example, it uses natural language processing technology to analyze the user's search history and the content of articles they have viewed to identify what themes the user is interested in. It can also use machine learning algorithms to analyze the user's past behavior history and predict topics they may be interested in in the future. This allows the analysis unit to create the optimal menu based on the user's hobbies and preferences. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the menu. For example, it can analyze how the user reacted to the suggested menu and adjust the next suggestion based on the results. This allows the analysis unit to always provide the user with the optimal menu and maximize the overall effectiveness of the system.

[0032] The suggestion department makes specific suggestions based on the menus created by the analysis department. Specifically, it suggests appropriate games and travel plans based on topics that the user is interested in. For example, if a user is interested in history, it can suggest history-related games or travel plans to visit historical sites. If a user is interested in maintaining their health, it can also suggest appropriate health maintenance methods. This includes specific exercise programs, meal plans, and relaxation methods. Furthermore, the suggestion department can suggest appropriate club activities and events based on the user's hobbies and interests. For example, if a user is interested in music, it can provide information on local music clubs and concerts. If a user wants to learn a new skill, it can also suggest information on online courses and workshops. The suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can analyze how users reacted to suggested activities and adjust the next suggestions based on the results. In this way, the suggestion department can always provide the best possible suggestions to the user and maximize the overall effectiveness of the system.

[0033] The service provider delivers the menus proposed by the suggestion provider. Specifically, they provide appropriate health maintenance methods based on topics of interest to the user. For example, they can offer appropriate games or travel plans based on topics of interest to the user. This includes links where the user can download suggested games and detailed information about travel plans. The service provider can also offer appropriate club activities and events based on the user's hobbies and interests. For example, they can provide registration links for users to participate in suggested club activities and detailed information about events. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, they can analyze how users reacted to the provided menus and adjust the content of the next delivery based on the results. This allows the service provider to always provide the optimal menu to the user and maximize the effectiveness of the entire system. In addition, the service provider can reliably transmit information using multiple communication methods. For example, they can use a combination of email, SMS, and in-app notifications to ensure that important information is delivered reliably. This allows the service provider to deliver menus to users quickly and reliably, maximizing the effectiveness of dementia prevention.

[0034] The data collection unit can collect the user's search history and topics of interest. For example, the data collection unit can analyze the user's search history and identify topics of interest. The data collection unit can also collect data such as events the user has attended and articles they have viewed. This enables data collection based on the user's interests. 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 the user's search history into AI and have the AI ​​identify topics of interest.

[0035] The proposal department can facilitate human interaction in clubs where AI acts as a facilitator. For example, the proposal department can propose club activities facilitated by AI, and users can participate to facilitate human interaction. The proposal department can also propose online communities facilitated by AI, and users can participate to facilitate human interaction. Furthermore, the proposal department can propose events facilitated by AI, and users can participate to facilitate human interaction. In this way, AI facilitating human interaction contributes to the prevention of dementia. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have AI execute proposals for club activities facilitated by AI.

[0036] The proposal unit can provide travel plans created by an AI planner. For example, the proposal unit can propose travel plans created by an AI planner, and user participation can contribute to dementia prevention. The proposal unit can also customize travel plans created by an AI planner and provide travel plans tailored to the user's interests. Furthermore, the proposal unit can update travel plans created by an AI planner in real time, providing the latest information. In this way, by providing travel plans created by AI, it can attract user interest and contribute to dementia prevention. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can have an AI execute the proposal of travel plans created by an AI planner.

[0037] The data collection unit can analyze the user's past search history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant data based on keywords that the user frequently searches for. The data collection unit can also select data to collect at specific time periods based on the user's search history. Furthermore, the data collection unit can analyze the user's search patterns and propose efficient data collection methods. This enables efficient data collection based on the user's past search history. 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 the user's past search history into AI and have the AI ​​select the optimal data collection method.

[0038] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can filter data based on topics the user is currently interested in. The data collection unit can also prioritize the collection of data relevant to the user's lifestyle (work, family, etc.). Furthermore, the data collection unit can filter data based on the user's current activity level (exercise, rest, etc.). This enables data collection tailored to the user's current situation. 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 the user's current lifestyle and areas of interest into an AI and have the AI ​​perform the data filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information related to the user's current location. It can also collect information on nearby tourist attractions and restaurants based on the user's location information. Furthermore, it can collect local news and weather information based on the user's location information. This enables the collection of highly relevant data based on the user's location information. 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 the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0040] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on topics the user has shown interest in on social media. It can also collect relevant data based on information shared by the user's social media friends. Furthermore, the data collection unit can analyze the user's social media activity history and collect data that is likely to be of interest. This enables data collection based on the user's social media activity. 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 the user's social media activity into AI and have AI perform the collection of relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specialized health analysis algorithm to health data. It can also apply an interest-based analysis algorithm to hobby data. Furthermore, it can apply a relationship-based analysis algorithm to social data. This enables appropriate analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​apply different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection period. This enables analysis tailored to the data collection period. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI ​​and have the AI ​​determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables analysis according to the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0045] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it can provide detailed explanations for high-importance proposals and concise explanations for low-importance proposals. Furthermore, the proposal department can determine the priority of proposals according to their importance. This enables proposals to be tailored to the importance of their content. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the proposal content into the AI ​​and have the AI ​​adjust the level of detail of the proposal.

[0046] The proposal unit can apply different proposal algorithms depending on the category of the proposal content. For example, the proposal unit can apply a specialized health algorithm to proposals related to health maintenance. It can also apply an interest-generating algorithm to proposals related to hobbies. Furthermore, it can apply a relationship-oriented algorithm to proposals related to social activities. This enables appropriate proposals according to the category of the proposal content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the proposal content into the AI ​​and have the AI ​​apply different proposal algorithms.

[0047] The proposal department can determine the priority of proposals based on when the proposal content was collected. For example, the proposal department can prioritize providing the most recent proposals. The proposal department can also make the latest proposals while referring to past proposals. Furthermore, the proposal department can adjust the priority of proposals according to when the proposal content was collected. This makes it possible to make proposals that are tailored to the timing of proposal content collection. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of proposal content collection into the AI ​​and have the AI ​​determine the priority of proposals.

[0048] The proposal unit can adjust the order of proposals based on their relevance. For example, the proposal unit may prioritize providing highly relevant proposals. It can also postpone less relevant proposals. Furthermore, the proposal unit can adjust the order of proposals according to their relevance. This enables proposals that are tailored to the relevance of their content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the proposals into the AI ​​and have the AI ​​adjust the order of the proposals.

[0049] The service delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, the service delivery unit may prioritize delivery methods that the user has preferred in the past. The service delivery unit can also select the optimal delivery timing based on the user's behavior history. Furthermore, the service delivery unit can analyze the user's past behavior patterns and propose efficient delivery methods. This enables the provision of the optimal delivery method based on the user's past behavior history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's past behavior history into AI and have the AI ​​select the optimal delivery method.

[0050] The service provider can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is at work, the service provider may select a delivery method that can be understood in a short time. If the user is relaxed, the service provider may also select a delivery method that includes detailed explanations. Furthermore, if the user is on the move, the service provider may select an audio delivery method. This makes it possible to customize the delivery method according to the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's current living situation into the AI ​​and have the AI ​​perform the customization of the delivery method.

[0051] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing information related to the user's current location. The service provider can also provide information on nearby events based on the user's location information. Furthermore, the service provider can provide local news and weather information based on the user's location information. This enables the provision of information in an optimal manner based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0052] The service provider can analyze the user's social media activity and propose delivery methods at the time of delivery. For example, the service provider can determine the content to be delivered based on topics the user has shown interest in on social media. The service provider can also provide relevant content based on information shared by the user's friends on social media. Furthermore, the service provider can analyze the user's social media activity history and provide content that is likely to be of interest. This makes it possible to propose delivery methods based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​propose delivery methods.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The dementia prevention system can further monitor the user's sleep patterns, and the analysis unit can analyze this data to make suggestions for improving the user's sleep quality. For example, the data collection unit monitors the user's sleep duration and depth, and the analysis unit analyzes this data to suggest the user's optimal sleep environment and sleep habits. Based on the analysis results, the suggestion unit can suggest an appropriate sleep environment (e.g., appropriate temperature and music) to the user. Furthermore, the provision unit can provide the user with specific methods for providing the suggested sleep environment (e.g., setting up smart home devices). This can improve the user's sleep quality and contribute to dementia prevention.

[0055] The dementia prevention system can further collect user dietary data, and the analysis unit can analyze this data to make suggestions for improving the user's nutritional balance. For example, the collection unit records the user's meals and calorie intake, and the analysis unit analyzes this data to create an optimal meal plan for the user. Based on the analysis results, the suggestion unit can suggest appropriate meal menus and nutritional supplements to the user. In addition, the provision unit can provide the user with specific methods for delivering the suggested meal menus (e.g., recipes and where to purchase ingredients). This can improve the user's nutritional balance and contribute to the prevention of dementia.

[0056] The dementia prevention system can further collect user exercise data, and the analysis unit can analyze this data to make suggestions for improving the user's exercise habits. For example, the collection unit records the user's exercise volume and type, and the analysis unit analyzes this data to create an optimal exercise plan for the user. Based on the analysis results, the suggestion unit can propose appropriate exercise menus and methods to the user. Furthermore, the provision unit can provide specific methods for delivering the suggested exercise menu to the user (e.g., exercise instruction or use of a fitness app). This can improve the user's exercise habits and contribute to dementia prevention.

[0057] The dementia prevention system can further monitor the user's stress level, and the analysis unit can analyze this data to suggest ways to reduce the user's stress. For example, the data collection unit can monitor the user's heart rate and stress hormone levels, and the analysis unit can analyze this data to suggest the most suitable stress reduction methods for the user. Based on the analysis results, the suggestion unit can suggest appropriate relaxation methods and stress management techniques to the user. Furthermore, the provision unit can provide the user with specific methods to implement the suggested relaxation methods (e.g., meditation apps or relaxation music). This can reduce the user's stress and contribute to dementia prevention.

[0058] The dementia prevention system can further suggest new hobbies and activities to users based on their interests and preferences. For example, the data collection unit collects data on the user's hobbies and interests, and the analysis unit analyzes this data to suggest the most suitable new hobbies and activities for the user. Based on the analysis results, the suggestion unit can suggest appropriate new hobbies and activities (e.g., art classes or sports clubs) to the user. The provision unit can also provide specific methods for delivering the suggested new hobbies and activities to the user (e.g., class reservation links or club participation instructions). This allows the system to suggest new activities based on the user's interests and preferences, contributing to dementia prevention.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects data about the user's hobbies and thoughts. For example, it collects data such as the user's search history, topics of interest, events attended, and articles viewed. Step 2: The analysis unit analyzes the data collected by the collection unit and creates a menu optimized for the user. For example, it analyzes the user's hobbies and preferences based on the collected data and creates an optimal menu. Step 3: The proposal team makes specific suggestions based on the menu created by the analysis team. For example, they suggest appropriate games, travel plans, health maintenance methods, club activities, and events based on topics the user is interested in. Step 4: The service department provides the menu proposed by the proposal department. For example, based on topics of interest to the user, they provide appropriate health maintenance methods, games and travel plans, club activities and events.

[0061] (Example of form 2) The dementia prevention system according to an embodiment of the present invention is a mobile application that utilizes AI to provide personalized dementia prevention menus. This dementia prevention system prevents dementia by collecting, analyzing, suggesting, and providing data related to the user's hobbies and thoughts. Specifically, the collection unit collects data related to the user's hobbies and thoughts, the analysis unit analyzes the collected data and creates an optimal menu for the user, the suggestion unit makes specific suggestions based on the analysis results, and the provision unit provides the suggested menu to the user. This service prevents dementia in the following three ways: 1. Prevention through social connections, 2. Prevention through suggestions such as games and travel, and 3. Prevention through maintaining health. For example, the collection unit collects the user's search history and topics of interest. Next, the analysis unit analyzes the collected data and creates an optimal menu for the user. For example, it suggests an appropriate menu based on the user's interests. The suggestion unit makes specific suggestions based on the analysis results. For example, it suggests appropriate games or travel plans based on the user's interests. Finally, the provision unit provides the suggested menu to the user. For example, it suggests appropriate methods for maintaining health based on the user's interests. Thus, AI-powered dementia prevention services provide personalized menus based on the user's hobbies and preferences, preventing dementia in three ways: through social connection, game and travel suggestions, and health maintenance. This allows the dementia prevention system to provide personalized dementia prevention menus based on the user's hobbies and preferences.

[0062] The dementia prevention system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a provision unit. The data collection unit collects data related to the user's hobbies and thoughts. For example, the data collection unit collects the user's search history and topics of interest. For example, the data collection unit can analyze the user's search history and identify topics of interest. The data collection unit can also collect data such as events the user has participated in and articles the user has viewed. The analysis unit analyzes the data collected by the data collection unit and creates an optimal menu for the user. For example, the analysis unit analyzes the user's hobbies and thoughts based on the collected data and creates an optimal menu. For example, the analysis unit can suggest an appropriate menu based on topics the user is interested in. The analysis unit can also analyze the user's past behavioral history and create an optimal menu. The suggestion unit makes specific suggestions based on the menu created by the analysis unit. For example, the suggestion unit can suggest appropriate games or travel plans based on topics the user is interested in. For example, the suggestion unit can suggest appropriate methods for maintaining health based on topics the user is interested in. Furthermore, the suggestion unit can also suggest appropriate club activities and events based on the user's hobbies and preferences. The provision unit provides the menus suggested by the suggestion unit. The provision unit can, for example, provide appropriate methods for maintaining health based on topics the user is interested in. The provision unit can, for example, provide appropriate games and travel plans based on topics the user is interested in. Furthermore, the provision unit can also provide appropriate club activities and events based on the user's hobbies and preferences. As a result, the dementia prevention system according to the embodiment can provide a personalized dementia prevention menu based on the user's hobbies and preferences.

[0063] The data collection unit collects data related to users' hobbies and interests. Specifically, it collects users' search history and topics of interest. For example, it analyzes keywords users have searched for on the internet and the history of web pages they have viewed to identify topics that users are interested in. It can also collect data on events users have attended and articles they have viewed. This includes information on webinars and seminars users have attended online, or events they have actually visited. Furthermore, posts that users have "liked" or shared on social media, and articles they have commented on, are also collected. This allows the data collection unit to understand users' interests and preferences from multiple perspectives. The collected data is stored on a cloud server and made accessible to the analysis unit. The frequency and accuracy of data collection can be adjusted according to user settings and system requirements, allowing for flexible responses to specific situations and conditions. For example, if a user starts a new hobby, information about it can be quickly collected, improving the overall personalization of the system.

[0064] The analysis unit analyzes the data collected by the data collection unit to create the optimal menu for the user. Specifically, it analyzes the user's hobbies and preferences based on the collected data to create the optimal menu. For example, it can suggest appropriate menus based on topics the user is interested in. The analysis unit uses AI to analyze data and gain a deep understanding of the user's interests and preferences. For example, it uses natural language processing technology to analyze the user's search history and the content of articles they have viewed to identify what themes the user is interested in. It can also use machine learning algorithms to analyze the user's past behavior history and predict topics they may be interested in in the future. This allows the analysis unit to create the optimal menu based on the user's hobbies and preferences. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the menu. For example, it can analyze how the user reacted to the suggested menu and adjust the next suggestion based on the results. This allows the analysis unit to always provide the user with the optimal menu and maximize the overall effectiveness of the system.

[0065] The suggestion department makes specific suggestions based on the menus created by the analysis department. Specifically, it suggests appropriate games and travel plans based on topics that the user is interested in. For example, if a user is interested in history, it can suggest history-related games or travel plans to visit historical sites. If a user is interested in maintaining their health, it can also suggest appropriate health maintenance methods. This includes specific exercise programs, meal plans, and relaxation methods. Furthermore, the suggestion department can suggest appropriate club activities and events based on the user's hobbies and interests. For example, if a user is interested in music, it can provide information on local music clubs and concerts. If a user wants to learn a new skill, it can also suggest information on online courses and workshops. The suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can analyze how users reacted to suggested activities and adjust the next suggestions based on the results. In this way, the suggestion department can always provide the best possible suggestions to the user and maximize the overall effectiveness of the system.

[0066] The service provider delivers the menus proposed by the suggestion provider. Specifically, they provide appropriate health maintenance methods based on topics of interest to the user. For example, they can offer appropriate games or travel plans based on topics of interest to the user. This includes links where the user can download suggested games and detailed information about travel plans. The service provider can also offer appropriate club activities and events based on the user's hobbies and interests. For example, they can provide registration links for users to participate in suggested club activities and detailed information about events. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, they can analyze how users reacted to the provided menus and adjust the content of the next delivery based on the results. This allows the service provider to always provide the optimal menu to the user and maximize the effectiveness of the entire system. In addition, the service provider can reliably transmit information using multiple communication methods. For example, they can use a combination of email, SMS, and in-app notifications to ensure that important information is delivered reliably. This allows the service provider to deliver menus to users quickly and reliably, maximizing the effectiveness of dementia prevention.

[0067] The data collection unit can collect the user's search history and topics of interest. For example, the data collection unit can analyze the user's search history and identify topics of interest. The data collection unit can also collect data such as events the user has attended and articles they have viewed. This enables data collection based on the user's interests. 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 the user's search history into AI and have the AI ​​identify topics of interest.

[0068] The proposal department can facilitate human interaction in clubs where AI acts as a facilitator. For example, the proposal department can propose club activities facilitated by AI, and users can participate to facilitate human interaction. The proposal department can also propose online communities facilitated by AI, and users can participate to facilitate human interaction. Furthermore, the proposal department can propose events facilitated by AI, and users can participate to facilitate human interaction. In this way, AI facilitating human interaction contributes to the prevention of dementia. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have AI execute proposals for club activities facilitated by AI.

[0069] The proposal unit can provide travel plans created by an AI planner. For example, the proposal unit can propose travel plans created by an AI planner, and user participation can contribute to dementia prevention. The proposal unit can also customize travel plans created by an AI planner and provide travel plans tailored to the user's interests. Furthermore, the proposal unit can update travel plans created by an AI planner in real time, providing the latest information. In this way, by providing travel plans created by AI, it can attract user interest and contribute to dementia prevention. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can have an AI execute the proposal of travel plans created by an AI planner.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during a time when the user can relax. The data collection unit can also delay data collection until the user calms down if they are excited. Furthermore, if the user is tired, the data collection unit can collect data after they have rested. This allows for data collection at an appropriate time according to the user's emotions. 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.

[0071] The data collection unit can analyze the user's past search history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant data based on keywords that the user frequently searches for. The data collection unit can also select data to collect at specific time periods based on the user's search history. Furthermore, the data collection unit can analyze the user's search patterns and propose efficient data collection methods. This enables efficient data collection based on the user's past search history. 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 the user's past search history into AI and have the AI ​​select the optimal data collection method.

[0072] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can filter data based on topics the user is currently interested in. The data collection unit can also prioritize the collection of data relevant to the user's lifestyle (work, family, etc.). Furthermore, the data collection unit can filter data based on the user's current activity level (exercise, rest, etc.). This enables data collection tailored to the user's current situation. 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 the user's current lifestyle and areas of interest into an AI and have the AI ​​perform the data filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting data related to their hobbies. If the user is stressed, the data collection unit may also prioritize collecting relaxing content. Furthermore, if the user is excited, the data collection unit may prioritize collecting data that interests them. This enables efficient data collection by prioritizing data according to the user's emotions. 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.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information related to the user's current location. It can also collect information on nearby tourist attractions and restaurants based on the user's location information. Furthermore, it can collect local news and weather information based on the user's location information. This enables the collection of highly relevant data based on the user's location information. 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 the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0075] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on topics the user has shown interest in on social media. It can also collect relevant data based on information shared by the user's social media friends. Furthermore, the data collection unit can analyze the user's social media activity history and collect data that is likely to be of interest. This enables data collection based on the user's social media activity. 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 the user's social media activity into AI and have AI perform the collection of relevant data.

[0076] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. This makes it possible to provide analysis results that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specialized health analysis algorithm to health data. It can also apply an interest-based analysis algorithm to hobby data. Furthermore, it can apply a relationship-based analysis algorithm to social data. This enables appropriate analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​apply different analysis algorithms.

[0079] 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 provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. This makes it possible to provide analysis results that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection period. This enables analysis tailored to the data collection period. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI ​​and have the AI ​​determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables analysis according to the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0082] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. This enables suggestions tailored to the user's emotions. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it can provide detailed explanations for high-importance proposals and concise explanations for low-importance proposals. Furthermore, the proposal department can determine the priority of proposals according to their importance. This enables proposals to be tailored to the importance of their content. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the proposal content into the AI ​​and have the AI ​​adjust the level of detail of the proposal.

[0084] The proposal unit can apply different proposal algorithms depending on the category of the proposal content. For example, the proposal unit can apply a specialized health algorithm to proposals related to health maintenance. It can also apply an interest-generating algorithm to proposals related to hobbies. Furthermore, it can apply a relationship-oriented algorithm to proposals related to social activities. This enables appropriate proposals according to the category of the proposal content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the proposal content into the AI ​​and have the AI ​​apply different proposal algorithms.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a more detailed suggestion. Furthermore, if the user is excited, the suggestion unit can provide a visually appealing suggestion. This enables suggestions tailored to the user's emotions. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The proposal department can determine the priority of proposals based on when the proposal content was collected. For example, the proposal department can prioritize providing the most recent proposals. The proposal department can also make the latest proposals while referring to past proposals. Furthermore, the proposal department can adjust the priority of proposals according to when the proposal content was collected. This makes it possible to make proposals that are tailored to the timing of proposal content collection. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of proposal content collection into the AI ​​and have the AI ​​determine the priority of proposals.

[0087] The proposal unit can adjust the order of proposals based on their relevance. For example, the proposal unit may prioritize providing highly relevant proposals. It can also postpone less relevant proposals. Furthermore, the proposal unit can adjust the order of proposals according to their relevance. This enables proposals that are tailored to the relevance of their content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the proposals into the AI ​​and have the AI ​​adjust the order of the proposals.

[0088] The service provider can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider may select a delivery method that includes detailed explanations. If the user is stressed, the service provider may select a concise and to-the-point delivery method. Furthermore, if the user is excited, the service provider may select a visually appealing delivery method. This enables delivery methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The service delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, the service delivery unit may prioritize delivery methods that the user has preferred in the past. The service delivery unit can also select the optimal delivery timing based on the user's behavior history. Furthermore, the service delivery unit can analyze the user's past behavior patterns and propose efficient delivery methods. This enables the provision of the optimal delivery method based on the user's past behavior history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's past behavior history into AI and have the AI ​​select the optimal delivery method.

[0090] The service provider can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is at work, the service provider may select a delivery method that can be understood in a short time. If the user is relaxed, the service provider may also select a delivery method that includes detailed explanations. Furthermore, if the user is on the move, the service provider may select an audio delivery method. This makes it possible to customize the delivery method according to the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's current living situation into the AI ​​and have the AI ​​perform the customization of the delivery method.

[0091] The service provider can estimate the user's emotions and prioritize the content offered based on those emotions. For example, if the user is relaxed, the service provider may prioritize detailed content. If the user is stressed, the service provider may prioritize concise content. Furthermore, if the user is excited, the service provider may prioritize visually appealing content. This makes it possible to prioritize content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing information related to the user's current location. The service provider can also provide information on nearby events based on the user's location information. Furthermore, the service provider can provide local news and weather information based on the user's location information. This enables the provision of information in an optimal manner based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0093] The service provider can analyze the user's social media activity and propose delivery methods at the time of delivery. For example, the service provider can determine the content to be delivered based on topics the user has shown interest in on social media. The service provider can also provide relevant content based on information shared by the user's friends on social media. Furthermore, the service provider can analyze the user's social media activity history and provide content that is likely to be of interest. This makes it possible to propose delivery methods based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​propose delivery methods.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The dementia prevention system can further monitor the user's sleep patterns, and the analysis unit can analyze this data to make suggestions for improving the user's sleep quality. For example, the data collection unit monitors the user's sleep duration and depth, and the analysis unit analyzes this data to suggest the user's optimal sleep environment and sleep habits. Based on the analysis results, the suggestion unit can suggest an appropriate sleep environment (e.g., appropriate temperature and music) to the user. Furthermore, the provision unit can provide the user with specific methods for providing the suggested sleep environment (e.g., setting up smart home devices). This can improve the user's sleep quality and contribute to dementia prevention.

[0096] The dementia prevention system can further collect user dietary data, and the analysis unit can analyze this data to make suggestions for improving the user's nutritional balance. For example, the collection unit records the user's meals and calorie intake, and the analysis unit analyzes this data to create an optimal meal plan for the user. Based on the analysis results, the suggestion unit can suggest appropriate meal menus and nutritional supplements to the user. In addition, the provision unit can provide the user with specific methods for delivering the suggested meal menus (e.g., recipes and where to purchase ingredients). This can improve the user's nutritional balance and contribute to the prevention of dementia.

[0097] The dementia prevention system can further collect user exercise data, and the analysis unit can analyze this data to make suggestions for improving the user's exercise habits. For example, the collection unit records the user's exercise volume and type, and the analysis unit analyzes this data to create an optimal exercise plan for the user. Based on the analysis results, the suggestion unit can propose appropriate exercise menus and methods to the user. Furthermore, the provision unit can provide specific methods for delivering the suggested exercise menu to the user (e.g., exercise instruction or use of a fitness app). This can improve the user's exercise habits and contribute to dementia prevention.

[0098] The dementia prevention system can further monitor the user's stress level, and the analysis unit can analyze this data to suggest ways to reduce the user's stress. For example, the data collection unit can monitor the user's heart rate and stress hormone levels, and the analysis unit can analyze this data to suggest the most suitable stress reduction methods for the user. Based on the analysis results, the suggestion unit can suggest appropriate relaxation methods and stress management techniques to the user. Furthermore, the provision unit can provide the user with specific methods to implement the suggested relaxation methods (e.g., meditation apps or relaxation music). This can reduce the user's stress and contribute to dementia prevention.

[0099] The dementia prevention system can further estimate the user's emotions and, based on those emotions, provide appropriate mental health care. For example, the data collection unit estimates emotions from the user's facial expressions and voice, and the analysis unit analyzes this data to create an optimal mental health care plan for the user. The proposal unit can then suggest appropriate counseling or mental health apps to the user based on the analysis results. The delivery unit can also provide the user with specific methods for delivering the suggested mental health care (e.g., online counseling booking or a link to download a mental health app). This can improve the user's mental health and contribute to dementia prevention.

[0100] The dementia prevention system can further estimate the user's emotions and, based on those emotions, provide the user with appropriate entertainment content. For example, the data collection unit estimates emotions from the user's facial expressions and voice, and the analysis unit analyzes that data to suggest the most suitable entertainment content for the user. The suggestion unit can suggest appropriate movies, music, or games to the user based on the analysis results. The delivery unit can also provide specific methods for delivering the suggested entertainment content to the user (e.g., links to streaming services or game download links). This allows the system to provide entertainment content tailored to the user's emotions and contribute to dementia prevention.

[0101] The dementia prevention system can further estimate the user's emotions and, based on those emotions, suggest appropriate communication methods. For example, the data collection unit estimates emotions from the user's facial expressions and voice, and the analysis unit analyzes that data to suggest the most suitable communication method for the user. The suggestion unit can then suggest appropriate communication methods (e.g., phone calls, video calls, messaging apps) based on the analysis results. The provision unit can also provide the user with specific methods for delivering the suggested communication methods (e.g., download links for video call apps or instructions for setting up messaging apps). This allows the system to provide communication methods tailored to the user's emotions and contribute to dementia prevention.

[0102] The dementia prevention system can further estimate the user's emotions and provide appropriate feedback based on those emotions. For example, the data collection unit estimates emotions from the user's facial expressions and voice, and the analysis unit analyzes that data to provide the user with optimal feedback. The suggestion unit can suggest appropriate feedback to the user (e.g., words of encouragement or advice) based on the analysis results. The delivery unit can also provide a specific method for delivering the suggested feedback to the user (e.g., a messaging app or voice assistant). This allows the system to provide feedback tailored to the user's emotions and contribute to dementia prevention.

[0103] The dementia prevention system can further estimate the user's emotions and provide appropriate learning content based on those emotions. For example, the data collection unit estimates emotions from the user's facial expressions and voice, and the analysis unit analyzes that data to suggest the most suitable learning content for the user. The suggestion unit can suggest appropriate learning content (e.g., online courses or educational apps) to the user based on the analysis results. The delivery unit can also provide specific methods for delivering the suggested learning content to the user (e.g., links to online courses or download links to educational apps). This allows the system to provide learning content that responds to the user's emotions and contribute to dementia prevention.

[0104] The dementia prevention system can further suggest new hobbies and activities to users based on their interests and preferences. For example, the data collection unit collects data on the user's hobbies and interests, and the analysis unit analyzes this data to suggest the most suitable new hobbies and activities for the user. Based on the analysis results, the suggestion unit can suggest appropriate new hobbies and activities (e.g., art classes or sports clubs) to the user. The provision unit can also provide specific methods for delivering the suggested new hobbies and activities to the user (e.g., class reservation links or club participation instructions). This allows the system to suggest new activities based on the user's interests and preferences, contributing to dementia prevention.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data collection unit collects data about the user's hobbies and thoughts. For example, it collects data such as the user's search history, topics of interest, events attended, and articles viewed. Step 2: The analysis unit analyzes the data collected by the collection unit and creates a menu optimized for the user. For example, it analyzes the user's hobbies and preferences based on the collected data and creates an optimal menu. Step 3: The proposal team makes specific suggestions based on the menu created by the analysis team. For example, they suggest appropriate games, travel plans, health maintenance methods, club activities, and events based on topics the user is interested in. Step 4: The service department provides the menu proposed by the proposal department. For example, based on topics of interest to the user, they provide appropriate health maintenance methods, games and travel plans, club activities and events.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data on the user's hobbies and thoughts using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to create a menu optimized for the user. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and makes specific suggestions based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14, for example, and provides the user with the proposed menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data on the user's hobbies and thoughts using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to create a menu optimized for the user. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and makes specific suggestions based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214, for example, and provides the user with the proposed menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on the user's hobbies and thoughts using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and create a menu optimized for the user. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to make specific suggestions based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, to provide the user with the proposed menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data on the user's hobbies and thoughts using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and create an optimal menu for the user. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to make specific suggestions based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414, for example, to provide the user with the proposed menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A data collection unit that collects data about users' hobbies and thoughts, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a menu optimized for the user, A proposal unit that makes specific suggestions based on the menu created by the aforementioned analysis unit, The system comprises a serving unit that provides the menu proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the user's search history and topics of interest. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, AI facilitates interaction between people in clubs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We offer travel plans created by an AI planner. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past search history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, 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 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When submitting a proposal, a different proposal algorithm is applied depending on the category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the proposal details were collected. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, adjust the order of the proposals based on their relevance. 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 delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, At the time of delivery, the system analyzes the user's past behavior history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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 data collection unit that collects data about users' hobbies and thoughts, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a menu optimized for the user, A proposal unit that makes specific suggestions based on the menu created by the aforementioned analysis unit, The system comprises a serving unit that provides the menu proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect the user's search history and topics of interest. The system according to feature 1.

3. The aforementioned proposal section is, Promoting human interaction in clubs facilitated by AI. The system according to feature 1.

4. The aforementioned proposal section is, We offer travel plans created by an AI planner. The system according to feature 1.

5. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past search history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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