system

Abstract

An object of a system according to an embodiment is to analyze user posts on an SNS, propose new ideas, and support profit improvement.SOLUTION: A system includes a post analysis part, an idea proposal part, a profit support part, and an information provision part. The post analysis unit analyzes a post of a user. The idea proposing section proposes a new idea based on the post of the user analyzed by the post analyzing section. The profit support unit proposes a profit improvement method based on the idea proposed by the idea proposal unit. The information providing part provides information in normal and emergency on the basis of the profit improvement method proposed by the profit support part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to effectively utilize user posts on social media to propose new ideas and link them to increased revenue.

[0005] The system according to the embodiment aims to analyze user posts on SNS, propose new ideas, and support profit improvement. [Means for solving the problem]

[0006] The system according to the embodiment includes a post analysis unit, an idea proposal unit, a revenue support unit, and an information provision unit. The post analysis unit analyzes user posts. The idea proposal unit proposes new ideas based on the user posts analyzed by the post analysis unit. The revenue support unit proposes methods for increasing revenue based on the ideas proposed by the idea proposal unit. The information provision unit provides information for peacetime and emergency situations based on the methods for increasing revenue proposed by the revenue support unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user posts on SNS, propose new ideas, and support increased profits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The SNS platform according to an embodiment of the present invention is a system that automatically analyzes user posts, uses generative AI to propose new ideas, provides ways to increase revenue, and provides information in both normal and emergency situations, thereby significantly improving the convenience and safety of users.

[0029] An SNS platform according to an embodiment includes a post analysis unit, an idea proposal unit, a revenue support unit, and an information provision unit. The post analysis unit analyzes user posts. For example, the post analysis unit analyzes text posts using natural language processing technology. The post analysis unit can also analyze image posts using image analysis technology. The post analysis unit can also analyze audio posts using audio analysis technology. For example, the post analysis unit performs keyword extraction and sentiment analysis in text analysis. In image analysis, the post content is classified using image recognition technology. In audio analysis, the post content is converted into text using audio recognition technology and analyzed. The idea proposal unit proposes new ideas based on the user posts analyzed by the post analysis unit. For example, the idea proposal unit proposes business ideas using a generation AI. The idea proposal unit can also propose technical ideas using the generation AI. The idea proposal unit can also propose creative ideas using the generation AI. For example, the generation AI generates related business ideas based on the user's post content. When generating technical ideas, relevant technical literature is referenced. When generating creative ideas, past creative works are referenced. The revenue support unit proposes methods for improving revenue based on ideas proposed by the idea proposal unit. For example, the revenue support unit proposes a marketing strategy. The revenue support unit can also propose cost-cutting measures. The revenue support unit can also propose an investment strategy. For example, the revenue support unit proposes a method for utilizing social media advertising as a marketing strategy. As a cost-cutting measure, it proposes an efficient resource allocation method. As an investment strategy, it proposes a portfolio for risk diversification. The information provision unit provides information in peacetime and in emergencies based on the revenue improvement methods proposed by the revenue support unit. For example, the information provision unit provides news based on the user's interests in peacetime. The information provision unit can also provide disaster information in emergencies. The information provision unit can also provide emergency contact information. For example, the information provision unit recommends news articles based on the user's interests in peacetime. In emergencies, it provides evacuation information in the event of a disaster.Local emergency contact information is provided as emergency contact information. This allows the SNS platform according to the embodiment to improve the convenience and safety of users. For example, users can obtain new business ideas and learn specific methods for increasing profits. Furthermore, important information can be obtained quickly in an emergency, allowing users to use the SNS with peace of mind.

[0030] The system includes a privacy protection unit that can analyze the content posted by a user, evaluate privacy risks, and display a warning. The privacy protection unit builds a system that evaluates privacy risks in real time when a user creates a post and displays a warning, for example, if personal information is included. The privacy protection unit also analyzes the content of the post and determines whether personal information is included. For example, if personal information such as a name, address, or phone number is included, a warning is displayed. The privacy protection unit can also evaluate privacy risks and display a warning before the user publishes the post, thereby protecting the user's privacy.

[0031] The data migration unit may provide an interface that facilitates data migration between different SNS platforms. The data migration unit may develop, for example, a function to automatically import data from different SNS platforms. For example, the data migration unit may enable users to easily transfer posts and images from other SNSs. The data migration unit may also provide an interface that facilitates data migration between different SNS platforms. For example, the data migration unit may import data using an API. The data migration unit may also enable users to easily transfer data using a GUI. This improves user convenience.

[0032] The system is equipped with a tagging unit that analyzes the content posted by users and automatically tags the content, thereby improving searchability. The tagging unit, for example, creates a system in which AI automatically analyzes content when a user posts and attaches appropriate tags. For example, tags are automatically attached to images. The tagging unit can also automatically attach tags to text posts. The tagging unit can also automatically attach tags to audio posts. For example, the tagging unit attaches tags to text posts using keyword extraction technology, tags to image posts using image recognition technology, and tags to audio posts using voice recognition technology. In this way, the content posted by users is analyzed and automatically tagged, thereby improving searchability.

[0033] The system includes a post analysis unit that analyzes the content of a user's posts and can automatically present related past posts and trend information. The post analysis unit, for example, analyzes the content of a user's posts and builds a system that automatically presents related past posts. For example, it displays past posts related to the same topic. The post analysis unit can also automatically present trend information. For example, it displays hashtag trends and news trends on SNS. The post analysis unit also automatically presents related past posts and trend information based on the content of a user's posts. For example, the post analysis unit displays past posts related to the content posted by the user. Current hashtag trends and news trends are displayed as trend information. This improves user convenience.

[0034] The system is provided with an interest prediction unit, which analyzes the content posted by a user, predicts their interests and concerns, and proposes ideas accordingly. The interest prediction unit, for example, analyzes the content posted by a user and builds a system that predicts their interests and concerns. For example, for a user who is interested in a particular topic, ideas related to that topic are proposed. The interest prediction unit can also analyze a user's past behavioral history to predict their interests and concerns. The interest prediction unit can also predict their interests and concerns based on survey results. For example, the interest prediction unit analyzes the content posted by a user and determines whether the user is interested in a particular topic. The interest prediction unit predicts the user's interests and concerns based on the past behavioral history. The interest prediction unit predicts the user's interests and concerns based on survey results. This makes it possible to predict the user's interests and concerns and propose ideas accordingly, thereby improving user satisfaction.

[0035] The system includes a feedback collection unit that can analyze user posted content and automatically collect feedback from experts in different fields. The feedback collection unit, for example, analyzes user posted content and builds a system that automatically collects feedback from experts in different fields. For example, the feedback collection unit collects feedback from technical experts for technical posts. The feedback collection unit can also collect feedback from business experts for business posts. The feedback collection unit can also collect feedback from creative experts for creative posts. For example, the feedback collection unit collects feedback from technical experts for technical posts. The feedback collection unit collects feedback from business experts for business posts. The feedback collection unit collects feedback from creative experts for creative posts. This can improve user convenience.

[0036] The system includes an event suggestion unit that can analyze the content of a user's posts and suggest related online events and workshops. The event suggestion unit, for example, builds a system that analyzes the content of a user's posts and suggests related online events and workshops. For example, a business workshop is suggested for a post about a business idea. The event suggestion unit can also suggest a technical workshop for a technical post. The event suggestion unit can also suggest a creative workshop for a creative post. For example, the event suggestion unit can suggest a business workshop for a post about a business idea. A technical workshop is suggested for a technical post. A creative workshop is suggested for a creative post. This can improve user convenience.

[0037] The system is provided with a revenue analysis unit, which can analyze a user's past revenue data and propose optimal revenue improvement measures. The revenue analysis unit, for example, analyzes a user's past revenue data and builds a system that proposes optimal revenue improvement measures. For example, it proposes an optimal investment strategy based on past revenue patterns. The revenue analysis unit can also propose a marketing strategy. The revenue analysis unit can also propose cost reduction measures. For example, the revenue analysis unit proposes an optimal investment strategy based on past revenue data. As a marketing strategy, it proposes how to utilize SNS advertising. As a cost reduction measure, it proposes an efficient resource allocation method. This makes it possible to improve the user's revenue.

[0038] The system is provided with an action plan generation unit, which can analyze the content of user posts and automatically generate specific action plans that are useful for increasing revenue. The action plan generation unit, for example, builds a system that analyzes the content of user posts and automatically generates specific action plans that are useful for increasing revenue. For example, for posts related to business ideas, a specific monetization strategy is proposed. The action plan generation unit can also propose a specific technology development plan for technical posts. The action plan generation unit can also propose a specific creative plan for creative posts. For example, the action plan generation unit proposes a specific monetization strategy for posts related to business ideas. A specific technology development plan is proposed for technical posts. A specific creative plan is proposed for creative posts. This makes it possible to increase the user's revenue.

[0039] The system is provided with a revenue model comparison unit, which can analyze different revenue models and propose the optimal model to the user. The revenue model comparison unit, for example, builds a system that analyzes different revenue models and proposes the optimal model to the user. For example, it compares subscription models and advertising models. The revenue model comparison unit can also propose the optimal revenue model based on the user's revenue data. The revenue model comparison unit can also compare the advantages and disadvantages of different revenue models. For example, the revenue model comparison unit compares the subscription model and advertising model and proposes the optimal model to the user. The revenue model is proposed based on the user's revenue data. The advantages and disadvantages of different revenue models are compared and the optimal model is proposed to the user. This can increase the user's revenue.

[0040] The system is provided with a training suggestion unit, which can analyze the content posted by users and suggest online courses and training programs that are useful for increasing revenue. The training suggestion unit, for example, builds a system that analyzes the content posted by users and suggests online courses and training programs that are useful for increasing revenue. For example, it suggests courses for improving business skills. The training suggestion unit can also suggest courses for improving technical skills. The training suggestion unit can also suggest courses for improving creative skills. For example, the training suggestion unit suggests online courses for improving business skills. It suggests online courses for improving technical skills. It suggests online courses for improving creative skills. This can increase the user's revenue.

[0041] The device is provided with a location information analysis unit, which analyzes the user's location information and can provide emergency information specific to the region. The location information analysis unit, for example, analyzes the user's location information and builds a system that provides emergency information specific to the region. For example, in the event of a disaster, information on local evacuation shelters is provided. The location information analysis unit can also provide local emergency contact information based on the user's location information. The location information analysis unit can also provide local disaster information. For example, the location information analysis unit provides local evacuation shelter information based on the user's location information. Local emergency contact information is provided. Local disaster information is provided. This can improve the safety of the user.

[0042] The information switching unit is provided, and the information switching unit can analyze the content posted by the user and automatically switch between information for normal times and emergency situations. The information switching unit, for example, analyzes the content posted by the user and builds a system that automatically switches between information for normal times and emergency situations. For example, in the event of a disaster, emergency information is displayed preferentially. The information switching unit can also provide news based on the user's interests in normal times. The information switching unit can also provide disaster information in the event of an emergency. For example, the information switching unit recommends news articles based on the user's interests in normal times. In the event of an emergency, it provides evacuation information in the event of a disaster. This can improve user convenience and safety.

[0043] The system includes a data integration unit that integrates data from different information sources to provide highly reliable emergency information. The data integration unit, for example, integrates data from different information sources to build a system that provides highly reliable emergency information. For example, the data integration unit integrates information from government agencies and news sites. The data integration unit can also prioritize integration of data from official agencies. The data integration unit also integrates data from different information sources to provide highly reliable emergency information. For example, the data integration unit integrates information from government agencies and news sites to provide highly reliable emergency information. The data integration unit prioritizes integration of data from official agencies to provide highly reliable emergency information. This can improve user safety.

[0044] The system includes a supplies suggestion unit that analyzes user postings to enable users to quickly find necessary supplies and support in an emergency. The supplies suggestion unit, for example, builds a system that enables users to quickly find necessary supplies and support in an emergency. For example, it provides information on evacuation shelters and relief supplies. The supplies suggestion unit can also analyze user postings to suggest necessary supplies in an emergency. The supplies suggestion unit can also analyze user postings to suggest necessary support in an emergency. For example, the supplies suggestion unit enables users to quickly find necessary supplies in an emergency. It provides information on relief supplies to enable users to quickly find necessary supplies. This can improve the safety of users.

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

[0046] The SNS platform can further include a health management unit that monitors the user's health status. The health management unit analyzes the user's postings and activity data to evaluate the user's health status. For example, the health management unit can estimate the user's stress level from the user's postings and suggest relaxation methods. The health management unit can also detect lack of exercise based on the user's activity data and suggest an exercise plan. Furthermore, the health management unit can analyze the user's diet and suggest measures to improve nutritional balance. This supports the user's health management and improves their quality of life.

[0047] The SNS platform may further include a learning support unit that manages the user's learning progress. The learning support unit analyzes the user's posted content and learning history to evaluate the user's learning progress. For example, the learning support unit may estimate the user's level of understanding of the learning material from the user's posted content and provide supplementary materials. The learning support unit may also propose a learning plan based on the user's learning history. Furthermore, the learning support unit may also propose a learning method that suits the user's learning style. This may improve the user's learning efficiency.

[0048] The SNS platform may further include a shopping suggestion unit that analyzes a user's purchasing history and makes personalized shopping suggestions. The shopping suggestion unit analyzes the user's postings and purchasing history to suggest products based on the user's interests and needs. For example, if the user is interested in fashion, the latest fashion items may be suggested. Also, if the user is interested in travel, travel-related products and services may be suggested. Furthermore, the shopping suggestion unit may suggest products related to products previously purchased based on the user's purchasing history. This can improve the user's purchasing experience.

[0049] The SNS platform can further include an academic support unit that analyzes the content posted by users and provides related academic papers and research materials. The academic support unit analyzes the content posted by users and automatically presents related academic papers and research materials. For example, if a user posts about a specific research topic, the academic support unit can provide the latest academic papers related to that topic. The academic support unit can also suggest reference materials according to the user's research field. Furthermore, the academic support unit can provide necessary materials according to the user's research progress. This supports the user's research activities and improves academic achievements.

[0050] The SNS platform may further include a local activity suggestion unit that analyzes the content posted by users and suggests local events and activities. The local activity suggestion unit analyzes the content posted by users and location information and suggests events and activities held in the local area. For example, if the user is interested in sports, it may suggest local sports events. Also, if the user is interested in cultural activities, it may suggest local cultural events and workshops. Furthermore, the local activity suggestion unit may suggest local volunteer activities based on the user's interests. This may encourage users to participate in local activities and contribute to the revitalization of the community.

[0051] The SNS platform may further include a travel suggestion unit that analyzes the content posted by users and proposes travel plans. The travel suggestion unit analyzes the content posted by users and their interests and proposes personalized travel plans. For example, if a user is interested in nature, it may propose tourist spots rich in nature. Also, if a user is interested in history, it may propose historical tourist spots. Furthermore, the travel suggestion unit may propose travel plans that suit the user's budget and schedule. This can improve the user's travel experience and increase satisfaction.

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

[0053] Step 1: The post analysis unit analyzes user posts. For example, the post analysis unit uses natural language processing technology to analyze text posts and perform keyword extraction and sentiment analysis. It also analyzes image posts using image analysis technology and classifies the post content using image recognition technology. It also analyzes voice posts using voice analysis technology and converts the post content into text using voice recognition technology, and then analyzes the text. Step 2: The idea suggestion unit proposes new ideas based on the user's posts analyzed by the post analysis unit. For example, it uses a generation AI to propose business ideas, technical ideas, and creative ideas. The generation AI generates related business ideas based on the content of the user's posts, refers to related technical literature when generating technical ideas, and refers to past creative works when generating creative ideas. Step 3: The Revenue Support Department proposes ways to improve revenue based on the ideas proposed by the Idea Proposal Department. For example, they propose marketing strategies, cost reduction measures, and investment strategies. Specifically, they propose ways to utilize social media advertising, efficient resource allocation methods, and portfolios for risk diversification. Step 4: The information provision unit provides information during normal times and emergencies based on the revenue improvement methods proposed by the revenue support unit. For example, during normal times, it recommends news articles based on the user's interests, and during emergencies, it provides evacuation information and local emergency contact information in the event of a disaster.

[0054] (Example 2) The SNS platform according to an embodiment of the present invention is a system that automatically analyzes user posts, uses generative AI to propose new ideas, provides ways to increase revenue, and provides information in both normal and emergency situations, thereby significantly improving the convenience and safety of users.

[0055] An SNS platform according to an embodiment includes a post analysis unit, an idea proposal unit, a revenue support unit, and an information provision unit. The post analysis unit analyzes user posts. For example, the post analysis unit analyzes text posts using natural language processing technology. The post analysis unit can also analyze image posts using image analysis technology. The post analysis unit can also analyze audio posts using audio analysis technology. For example, the post analysis unit performs keyword extraction and sentiment analysis in text analysis. In image analysis, the post content is classified using image recognition technology. In audio analysis, the post content is converted into text using audio recognition technology and analyzed. The idea proposal unit proposes new ideas based on the user posts analyzed by the post analysis unit. For example, the idea proposal unit proposes business ideas using a generation AI. The idea proposal unit can also propose technical ideas using the generation AI. The idea proposal unit can also propose creative ideas using the generation AI. For example, the generation AI generates related business ideas based on the user's post content. When generating technical ideas, relevant technical literature is referenced. When generating creative ideas, past creative works are referenced. The revenue support unit proposes methods for improving revenue based on ideas proposed by the idea proposal unit. For example, the revenue support unit proposes a marketing strategy. The revenue support unit can also propose cost-cutting measures. The revenue support unit can also propose an investment strategy. For example, the revenue support unit proposes a method for utilizing social media advertising as a marketing strategy. As a cost-cutting measure, it proposes an efficient resource allocation method. As an investment strategy, it proposes a portfolio for risk diversification. The information provision unit provides information in peacetime and in emergencies based on the revenue improvement methods proposed by the revenue support unit. For example, the information provision unit provides news based on the user's interests in peacetime. The information provision unit can also provide disaster information in emergencies. The information provision unit can also provide emergency contact information. For example, the information provision unit recommends news articles based on the user's interests in peacetime. In emergencies, it provides evacuation information in the event of a disaster.Local emergency contact information is provided as emergency contact information. This allows the SNS platform according to the embodiment to improve the convenience and safety of users. For example, users can obtain new business ideas and learn specific methods for increasing profits. Furthermore, important information can be obtained quickly in an emergency, allowing users to use the SNS with peace of mind.

[0056] The system includes a privacy protection unit that can analyze the content posted by a user, evaluate privacy risks, and display a warning. The privacy protection unit builds a system that evaluates privacy risks in real time when a user creates a post and displays a warning, for example, if personal information is included. The privacy protection unit also analyzes the content of the post and determines whether personal information is included. For example, if personal information such as a name, address, or phone number is included, a warning is displayed. The privacy protection unit can also evaluate privacy risks and display a warning before the user publishes the post, thereby protecting the user's privacy.

[0057] The system includes an emotion estimation unit that analyzes the content posted by a user, estimates the user's emotional state, and provides a feed customized according to the user's emotional state. The emotion estimation unit, for example, analyzes the content posted by the user and the browsing history of the user, and estimates the user's emotional state using the emotion estimation function. For example, positive content may be preferentially displayed to a user in a positive emotional state. The emotion estimation unit may also display encouraging messages to a user in a negative emotional state. The emotion estimation unit provides a feed customized according to the user's emotional state. For example, the emotion estimation unit may recommend positive news articles and videos to a user in a positive emotional state, and display encouraging messages and positive content to a user in a negative emotional state. This can improve user satisfaction.

[0058] The data migration unit may provide an interface that facilitates data migration between different SNS platforms. The data migration unit may develop, for example, a function to automatically import data from different SNS platforms. For example, the data migration unit may enable users to easily transfer posts and images from other SNSs. The data migration unit may also provide an interface that facilitates data migration between different SNS platforms. For example, the data migration unit may import data using an API. The data migration unit may also enable users to easily transfer data using a GUI. This improves user convenience.

[0059] The system is equipped with a tagging unit that analyzes the content posted by users and automatically tags the content, thereby improving searchability. The tagging unit, for example, creates a system in which AI automatically analyzes content when a user posts and attaches appropriate tags. For example, tags are automatically attached to images. The tagging unit can also automatically attach tags to text posts. The tagging unit can also automatically attach tags to audio posts. For example, the tagging unit attaches tags to text posts using keyword extraction technology, tags to image posts using image recognition technology, and tags to audio posts using voice recognition technology. In this way, the content posted by users is analyzed and automatically tagged, thereby improving searchability.

[0060] The system includes an emotion estimation unit that analyzes the content of a user's posts, estimates the user's emotions in real time, and makes suggestions to elicit positive emotions. For example, when a user creates a post, the emotion estimation unit uses the emotion estimation function to analyze the user's emotions in real time and makes suggestions to elicit positive emotions. For example, an encouraging message is displayed when a negative emotion is detected. The emotion estimation unit also analyzes the content of a user's posts and estimates the user's emotional state. For example, positive content is recommended to a user in a positive emotional state. The emotion estimation unit also makes suggestions to elicit positive emotions according to the user's emotional state. For example, the emotion estimation unit displays an encouraging message or positive content to elicit positive emotions. This can improve user satisfaction.

[0061] The system includes a post analysis unit that analyzes the content of a user's posts and can automatically present related past posts and trend information. The post analysis unit, for example, analyzes the content of a user's posts and builds a system that automatically presents related past posts. For example, it displays past posts related to the same topic. The post analysis unit can also automatically present trend information. For example, it displays hashtag trends and news trends on SNS. The post analysis unit also automatically presents related past posts and trend information based on the content of a user's posts. For example, the post analysis unit displays past posts related to the content posted by the user. Current hashtag trends and news trends are displayed as trend information. This improves user convenience.

[0062] The system is provided with an interest prediction unit, which analyzes the content posted by a user, predicts their interests and concerns, and proposes ideas accordingly. The interest prediction unit, for example, analyzes the content posted by a user and builds a system that predicts their interests and concerns. For example, for a user who is interested in a particular topic, ideas related to that topic are proposed. The interest prediction unit can also analyze a user's past behavioral history to predict their interests and concerns. The interest prediction unit can also predict their interests and concerns based on survey results. For example, the interest prediction unit analyzes the content posted by a user and determines whether the user is interested in a particular topic. The interest prediction unit predicts the user's interests and concerns based on the past behavioral history. The interest prediction unit predicts the user's interests and concerns based on survey results. This makes it possible to predict the user's interests and concerns and propose ideas accordingly, thereby improving user satisfaction.

[0063] The system includes an emotion estimation unit that analyzes the content posted by the user, suggests ideas based on the user's emotional state, and can elicit a positive response. The emotion estimation unit, for example, analyzes the content posted by the user and estimates the user's emotional state using the emotion estimation function. For example, the emotion estimation unit suggests positive ideas to a user in a positive emotional state. The emotion estimation unit can also display an encouraging message to a user in a negative emotional state. The emotion estimation unit also suggests ideas that elicit a positive response based on the user's emotional state. For example, the emotion estimation unit suggests positive ideas to a user in a positive emotional state, and displays encouraging messages or positive content to a user in a negative emotional state. This can improve user satisfaction.

[0064] The system includes a feedback collection unit that can analyze user posted content and automatically collect feedback from experts in different fields. The feedback collection unit, for example, analyzes user posted content and builds a system that automatically collects feedback from experts in different fields. For example, the feedback collection unit collects feedback from technical experts for technical posts. The feedback collection unit can also collect feedback from business experts for business posts. The feedback collection unit can also collect feedback from creative experts for creative posts. For example, the feedback collection unit collects feedback from technical experts for technical posts. The feedback collection unit collects feedback from business experts for business posts. The feedback collection unit collects feedback from creative experts for creative posts. This can improve user convenience.

[0065] The system includes an event suggestion unit that can analyze the content of a user's posts and suggest related online events and workshops. The event suggestion unit, for example, builds a system that analyzes the content of a user's posts and suggests related online events and workshops. For example, a business workshop is suggested for a post about a business idea. The event suggestion unit can also suggest a technical workshop for a technical post. The event suggestion unit can also suggest a creative workshop for a creative post. For example, the event suggestion unit can suggest a business workshop for a post about a business idea. A technical workshop is suggested for a technical post. A creative workshop is suggested for a creative post. This can improve user convenience.

[0066] The system includes an emotion estimation unit that analyzes the content posted by the user, suggests ideas according to the emotion, and promotes emotional empathy. The emotion estimation unit, for example, analyzes the content posted by the user and estimates the emotional state using the emotion estimation function. For example, the emotion estimation unit suggests positive ideas to a user in a positive emotional state. The emotion estimation unit can also display encouraging messages to a user in a negative emotional state. The emotion estimation unit also suggests ideas that promote emotional empathy based on the user's emotional state. For example, the emotion estimation unit suggests positive ideas to a user in a positive emotional state, and displays encouraging messages or positive content to a user in a negative emotional state. This can improve user satisfaction.

[0067] The system is provided with a revenue analysis unit, which can analyze a user's past revenue data and propose optimal revenue improvement measures. The revenue analysis unit, for example, analyzes a user's past revenue data and builds a system that proposes optimal revenue improvement measures. For example, it proposes an optimal investment strategy based on past revenue patterns. The revenue analysis unit can also propose a marketing strategy. The revenue analysis unit can also propose cost reduction measures. For example, the revenue analysis unit proposes an optimal investment strategy based on past revenue data. As a marketing strategy, it proposes how to utilize SNS advertising. As a cost reduction measure, it proposes an efficient resource allocation method. This makes it possible to improve the user's revenue.

[0068] The system is provided with an action plan generation unit, which can analyze the content of user posts and automatically generate specific action plans that are useful for increasing revenue. The action plan generation unit, for example, builds a system that analyzes the content of user posts and automatically generates specific action plans that are useful for increasing revenue. For example, for posts related to business ideas, a specific monetization strategy is proposed. The action plan generation unit can also propose a specific technology development plan for technical posts. The action plan generation unit can also propose a specific creative plan for creative posts. For example, the action plan generation unit proposes a specific monetization strategy for posts related to business ideas. A specific technology development plan is proposed for technical posts. A specific creative plan is proposed for creative posts. This makes it possible to increase the user's revenue.

[0069] The system includes an emotion estimation unit that analyzes the content posted by a user and suggests revenue improvement measures according to the user's emotional state, thereby increasing motivation. The emotion estimation unit, for example, analyzes the content posted by a user and estimates the user's emotional state using the emotion estimation function. For example, the emotion estimation unit suggests positive revenue improvement measures to a user in a positive emotional state. The emotion estimation unit can also display encouraging messages to a user in a negative emotional state. The emotion estimation unit also suggests revenue improvement measures based on the user's emotional state. For example, the emotion estimation unit suggests positive revenue improvement measures to a user in a positive emotional state, and suggests encouraging messages and positive revenue improvement measures to a user in a negative emotional state. This makes it possible to increase the user's revenue.

[0070] The system is provided with a revenue model comparison unit, which can analyze different revenue models and propose the optimal model to the user. The revenue model comparison unit, for example, builds a system that analyzes different revenue models and proposes the optimal model to the user. For example, it compares subscription models and advertising models. The revenue model comparison unit can also propose the optimal revenue model based on the user's revenue data. The revenue model comparison unit can also compare the advantages and disadvantages of different revenue models. For example, the revenue model comparison unit compares the subscription model and advertising model and proposes the optimal model to the user. The revenue model is proposed based on the user's revenue data. The advantages and disadvantages of different revenue models are compared and the optimal model is proposed to the user. This can increase the user's revenue.

[0071] The system is provided with a training suggestion unit, which can analyze the content posted by users and suggest online courses and training programs that are useful for increasing revenue. The training suggestion unit, for example, builds a system that analyzes the content posted by users and suggests online courses and training programs that are useful for increasing revenue. For example, it suggests courses for improving business skills. The training suggestion unit can also suggest courses for improving technical skills. The training suggestion unit can also suggest courses for improving creative skills. For example, the training suggestion unit suggests online courses for improving business skills. It suggests online courses for improving technical skills. It suggests online courses for improving creative skills. This can increase the user's revenue.

[0072] The system includes an emotion estimation unit that analyzes the content posted by a user, proposes revenue improvement measures based on the user's emotion, and can elicit positive emotions. The emotion estimation unit, for example, analyzes the content posted by a user and estimates the user's emotional state using the emotion estimation function. For example, the emotion estimation unit proposes positive revenue improvement measures to a user in a positive emotional state. The emotion estimation unit can also display encouraging messages to a user in a negative emotional state. The emotion estimation unit also proposes revenue improvement measures based on the user's emotional state. For example, the emotion estimation unit proposes positive revenue improvement measures to a user in a positive emotional state, and proposes encouraging messages and positive revenue improvement measures to a user in a negative emotional state. This can improve the user's revenue.

[0073] The device is provided with a location information analysis unit, which analyzes the user's location information and can provide emergency information specific to the region. The location information analysis unit, for example, analyzes the user's location information and builds a system that provides emergency information specific to the region. For example, in the event of a disaster, information on local evacuation shelters is provided. The location information analysis unit can also provide local emergency contact information based on the user's location information. The location information analysis unit can also provide local disaster information. For example, the location information analysis unit provides local evacuation shelter information based on the user's location information. Local emergency contact information is provided. Local disaster information is provided. This can improve the safety of the user.

[0074] The information switching unit is provided, and the information switching unit can analyze the content posted by the user and automatically switch between information for normal times and emergency situations. The information switching unit, for example, analyzes the content posted by the user and builds a system that automatically switches between information for normal times and emergency situations. For example, in the event of a disaster, emergency information is displayed preferentially. The information switching unit can also provide news based on the user's interests in normal times. The information switching unit can also provide disaster information in the event of an emergency. For example, the information switching unit recommends news articles based on the user's interests in normal times. In the event of an emergency, it provides evacuation information in the event of a disaster. This can improve user convenience and safety.

[0075] The device includes an emotion deduction unit that analyzes content posted by a user and provides emergency information according to the user's emotional state, thereby enhancing a sense of security. The emotion deduction unit, for example, analyzes content posted by a user and infers the user's emotional state using an emotion deduction function. For example, in an emergency, emergency information according to the user's emotional state is provided. The emotion deduction unit can also provide information to enhance a sense of security based on the user's emotional state. For example, if the user is feeling anxious, the emotion deduction unit displays a message to enhance a sense of security. The emotion deduction unit also provides emergency information according to the user's emotional state. For example, if the user is feeling anxious, the emotion deduction unit provides emergency information to enhance a sense of security. This enhances the user's safety.

[0076] The system includes a data integration unit that integrates data from different information sources to provide highly reliable emergency information. The data integration unit, for example, integrates data from different information sources to build a system that provides highly reliable emergency information. For example, the data integration unit integrates information from government agencies and news sites. The data integration unit can also prioritize integration of data from official agencies. The data integration unit also integrates data from different information sources to provide highly reliable emergency information. For example, the data integration unit integrates information from government agencies and news sites to provide highly reliable emergency information. The data integration unit prioritizes integration of data from official agencies to provide highly reliable emergency information. This can improve user safety.

[0077] The system includes a supplies suggestion unit that analyzes user postings to enable users to quickly find necessary supplies and support in an emergency. The supplies suggestion unit, for example, builds a system that enables users to quickly find necessary supplies and support in an emergency. For example, it provides information on evacuation shelters and relief supplies. The supplies suggestion unit can also analyze user postings to suggest necessary supplies in an emergency. The supplies suggestion unit can also analyze user postings to suggest necessary support in an emergency. For example, the supplies suggestion unit enables users to quickly find necessary supplies in an emergency. It provides information on relief supplies to enable users to quickly find necessary supplies. This can improve the safety of users.

[0078] The device includes an emotion deduction unit that analyzes content posted by a user and provides emergency information based on the user's emotion, thereby providing a sense of emotional security. The emotion deduction unit, for example, analyzes content posted by a user and estimates an emotional state using an emotion deduction function. For example, in an emergency, emergency information according to the user's emotional state is provided. The emotion deduction unit can also provide information for providing a sense of emotional security based on the user's emotional state. For example, if the user is feeling anxious, the emotion deduction unit displays a message to increase the sense of security. The emotion deduction unit also provides emergency information according to the user's emotional state. For example, if the user is feeling anxious, the emotion deduction unit provides emergency information to increase the sense of security. This can improve the user's safety.

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

[0080] The SNS platform can further include a health management unit that monitors the user's health status. The health management unit analyzes the user's postings and activity data to evaluate the user's health status. For example, the health management unit can estimate the user's stress level from the user's postings and suggest relaxation methods. The health management unit can also detect lack of exercise based on the user's activity data and suggest an exercise plan. Furthermore, the health management unit can analyze the user's diet and suggest measures to improve nutritional balance. This supports the user's health management and improves their quality of life.

[0081] The SNS platform may further include a learning support unit that manages the user's learning progress. The learning support unit analyzes the user's posted content and learning history to evaluate the user's learning progress. For example, the learning support unit may estimate the user's level of understanding of the learning material from the user's posted content and provide supplementary materials. The learning support unit may also propose a learning plan based on the user's learning history. Furthermore, the learning support unit may also propose a learning method that suits the user's learning style. This may improve the user's learning efficiency.

[0082] The SNS platform may further include a music recommendation unit that estimates a user's emotions and recommends music based on the emotions. The music recommendation unit analyzes the user's posted content and estimates the user's emotional state using the emotion estimation function. For example, energetic music may be recommended to a user in a positive emotional state. Relaxing music may also be recommended to a user in a negative emotional state. Furthermore, the music recommendation unit may recommend music to stabilize emotions according to the user's emotional state. This improves the user's emotional state and provides a relaxing time.

[0083] The SNS platform may further include a shopping suggestion unit that analyzes a user's purchasing history and makes personalized shopping suggestions. The shopping suggestion unit analyzes the user's postings and purchasing history to suggest products based on the user's interests and needs. For example, if the user is interested in fashion, the latest fashion items may be suggested. Also, if the user is interested in travel, travel-related products and services may be suggested. Furthermore, the shopping suggestion unit may suggest products related to products previously purchased based on the user's purchasing history. This can improve the user's purchasing experience.

[0084] The SNS platform may further include a fitness suggestion unit that estimates the user's emotions and suggests a fitness plan based on the emotions. The fitness suggestion unit analyzes the content posted by the user and estimates the user's emotional state using the emotion estimation function. For example, an energetic fitness plan may be suggested to a user in a positive emotional state. Alternatively, a relaxing yoga or stretching plan may be suggested to a user in a negative emotional state. Furthermore, the fitness suggestion unit may suggest a fitness plan to increase motivation according to the user's emotional state. This can support the user's health and fitness.

[0085] The SNS platform can further include an academic support unit that analyzes the content posted by users and provides related academic papers and research materials. The academic support unit analyzes the content posted by users and automatically presents related academic papers and research materials. For example, if a user posts about a specific research topic, the academic support unit can provide the latest academic papers related to that topic. The academic support unit can also suggest reference materials according to the user's research field. Furthermore, the academic support unit can provide necessary materials according to the user's research progress. This supports the user's research activities and improves academic achievements.

[0086] The SNS platform may further include a mental health support unit that estimates a user's emotions and provides emotionally based mental health support. The mental health support unit analyzes the user's posts and estimates their emotional state using the emotion estimation function. For example, a user in a negative emotional state may be offered counseling services or relaxation techniques. A user in a positive emotional state may be offered activities to maintain their emotions. Furthermore, the mental health support unit may provide information and resources related to mental health according to the user's emotional state. This may support the user's mental health and improve their overall well-being.

[0087] The SNS platform may further include a local activity suggestion unit that analyzes the content posted by users and suggests local events and activities. The local activity suggestion unit analyzes the content posted by users and location information and suggests events and activities held in the local area. For example, if the user is interested in sports, it may suggest local sports events. Also, if the user is interested in cultural activities, it may suggest local cultural events and workshops. Furthermore, the local activity suggestion unit may suggest local volunteer activities based on the user's interests. This may encourage users to participate in local activities and contribute to the revitalization of the community.

[0088] The SNS platform may further include a reading suggestion unit that estimates a user's emotions and suggests a reading list based on the emotions. The reading suggestion unit analyzes the user's posted content and estimates the user's emotional state using the emotion estimation function. For example, a user in a positive emotional state may be suggested books with energetic content. A user in a negative emotional state may be suggested books with relaxing content. Furthermore, the reading suggestion unit may suggest a reading list to stabilize the user's emotions according to the user's emotional state. This improves the user's reading experience and helps maintain emotional balance.

[0089] The SNS platform may further include a travel suggestion unit that analyzes the content posted by users and proposes travel plans. The travel suggestion unit analyzes the content posted by users and their interests and proposes personalized travel plans. For example, if a user is interested in nature, it may propose tourist spots rich in nature. Also, if a user is interested in history, it may propose historical tourist spots. Furthermore, the travel suggestion unit may propose travel plans that suit the user's budget and schedule. This can improve the user's travel experience and increase satisfaction.

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

[0091] Step 1: The post analysis unit analyzes user posts. For example, the post analysis unit uses natural language processing technology to analyze text posts and perform keyword extraction and sentiment analysis. It also analyzes image posts using image analysis technology and classifies the post content using image recognition technology. It also analyzes voice posts using voice analysis technology and converts the post content into text using voice recognition technology, and then analyzes the text. Step 2: The idea suggestion unit proposes new ideas based on the user's posts analyzed by the post analysis unit. For example, it uses a generation AI to propose business ideas, technical ideas, and creative ideas. The generation AI generates related business ideas based on the content of the user's posts, refers to related technical literature when generating technical ideas, and refers to past creative works when generating creative ideas. Step 3: The Revenue Support Department proposes ways to improve revenue based on the ideas proposed by the Idea Proposal Department. For example, they propose marketing strategies, cost reduction measures, and investment strategies. Specifically, they propose ways to utilize social media advertising, efficient resource allocation methods, and portfolios for risk diversification. Step 4: The information provision unit provides information during normal times and emergencies based on the revenue improvement methods proposed by the revenue support unit. For example, during normal times, it recommends news articles based on the user's interests, and during emergencies, it provides evacuation information and local emergency contact information in the event of a disaster.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0126] 7, 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a post analysis unit that analyzes user posts; an idea suggestion unit that suggests new ideas based on the posts of the users analyzed by the post analysis unit; a revenue support unit that proposes a method for improving revenue based on the idea proposed by the idea proposal unit; an information providing unit that provides information in normal times and in emergencies based on the method of increasing profits proposed by the profit support unit. A system characterized by:

2. An emotion estimation unit is provided, The emotion estimation unit Analyzing the user's posted content, estimating their emotional state, and providing a customized feed according to their emotional state.

2. The system of claim 1.

3. Equipped with a data transfer section, The data migration unit Provide an interface that facilitates data transfer between different SNS platforms 2. The system of claim 1.

4. Equipped with a post analysis section, The post analysis unit Analyze the user's posted content and automatically display related past posts and trend information.

2. The system of claim 1.

5. Equipped with a revenue analysis department, The profit analysis unit Analyze the user's past revenue data and propose optimal revenue improvement measures 2. The system of claim 1.

6. Equipped with a location information analysis unit, The location information analysis unit Analyze the user's location information and provide emergency information specific to the area 2. The system of claim 1.

7. An emotion estimation unit is provided, The emotion estimation unit Analyzing the content posted by the user and providing emergency information according to the user's emotional state to enhance a sense of security 2. The system of claim 1.

8. An emotion estimation unit is provided, The emotion estimation unit Analyzing the content posted by the user and providing emergency information based on emotions to provide emotional relief 2. The system of claim 1.

Images