System

Abstract

An object of a system according to an embodiment is to provide information according to a user's need in real time.SOLUTION: A system according to an embodiment includes a need analysis unit, a data collection unit, an information generation unit, and a provision unit. The need analysis unit analyzes a need of a user. The data collector collects data from an information source on the Internet based on the need analyzed by the need analyzer. The information generation unit generates information based on the data collected by the data collection unit. The providing unit provides the information generated by the information generating unit through the portal site or the smartphone application.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 technologies have difficulty in providing information that meets user needs in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information that meets the needs of the user in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a needs analysis unit, a data collection unit, an information generation unit, and a provision unit. The needs analysis unit analyzes user needs. The data collection unit collects data from information sources on the Internet based on the needs analyzed by the needs analysis unit. The information generation unit generates information based on the data collected by the data collection unit. The provision unit provides the information generated by the information generation unit via a portal site or smartphone app. [Effects of the Invention]

[0007] The system according to the embodiment can provide information that meets the needs of the user in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information provision system according to the embodiment of the present invention is a system that generates personal, niche, and customized summary information of data that users "want to know" in real time according to their individual needs, and provides it as a portal site or smartphone app. As a result, the information provision system can provide a "fountain of knowledge" that can reach all the information that users "want to know."

[0029] An information provision system according to an embodiment includes a needs analysis unit, a data collection unit, an information generation unit, and a provision unit. The needs analysis unit analyzes user needs. For example, the needs analysis unit analyzes information input by the user, past search history, and browsing history to identify the user's interests. The needs analysis unit can also analyze the user's behavioral patterns and predict potential needs. The data collection unit collects data from online information sources based on the needs analyzed by the needs analysis unit. For example, the data collection unit collects data in real time from news sites, blogs, social networking sites, etc. The data collection unit can also evaluate the reliability and credibility of the collected data and prioritize collection of highly reliable information. The information generation unit generates information based on the data collected by the data collection unit. For example, the information generation unit analyzes the collected data and extracts and provides information useful to the user. The information generation unit can also use an emotion estimation function to analyze the user's emotions regarding the collected data and generate information based on the emotions. The provision unit provides the information generated by the information generation unit through a portal site or a smartphone app. For example, the providing unit provides the generated information to the user in real time. The providing unit can also collect feedback from the user and improve the accuracy of the information provided based on the feedback. As a result, the information providing system according to the embodiment can provide information that meets the needs of the user in real time.

[0030] The needs analysis unit can predict potential needs by analyzing a user's lifestyle patterns and behavioral history. The needs analysis unit, for example, analyzes a user's lifestyle patterns and predicts potential needs based on behavioral history during specific times of the day or day of the week. For example, if a user has the habit of checking the news on weekday mornings, information related to that time period is provided. The needs analysis unit also analyzes a user's behavioral history and predicts future needs from past behavioral patterns. For example, if a user has a history of participating in a specific event, information related to that event is provided. The needs analysis unit also predicts needs according to changes in seasons and weather based on the user's lifestyle patterns. For example, information about heating appliances is provided in winter. This makes it possible to predict the user's potential needs.

[0031] The needs analysis unit can analyze a user's social media activity and track changes in interests in real time. For example, the needs analysis unit analyzes a user's social media posts and identifies changes in interests from the content of the posts and hashtags. For example, if there is an increase in posts about a new hobby, information related to that hobby is provided. The needs analysis unit also analyzes the user's following and like history on social media to track changes in interests. For example, if a user follows a new account, information related to that account is provided. The needs analysis unit also monitors a user's social media activity in real time and dynamically adjusts the information provided in accordance with changes in interests. For example, if there is an increase in posts about a particular topic, information related to that topic is provided. This makes it possible to track changes in a user's interests in real time.

[0032] The needs analysis unit can integrate data from different devices to identify more detailed needs. For example, the needs analysis unit analyzes heart rate and activity data from a smartwatch to identify needs based on the user's health condition and exercise habits. For example, it provides information about recovery after exercise. The needs analysis unit also analyzes the voice command history from a smart speaker to identify the user's interests. For example, it provides information about favorite genres from music playback history. The needs analysis unit also integrates data from different devices to understand the user's entire life and identify more detailed needs. For example, it identifies household needs based on data from smart home devices. This makes it possible to integrate data from different devices to identify detailed needs.

[0033] The needs analysis unit can compare the needs of users from different languages ​​and cultural regions and identify needs from a global perspective. For example, the needs analysis unit analyzes search histories and browsing histories in different languages ​​to compare the needs of users from each language region. For example, it compares the needs of users from English-speaking and Japanese-speaking regions and identifies similarities and differences. The needs analysis unit also analyzes the social media activities of users from different cultural regions and identifies needs based on cultural background. For example, it provides information related to holidays in a particular culture. The needs analysis unit also compares the needs of users from different regions from a global perspective and identifies common needs. For example, it analyzes demand for a particular product or service by region. This makes it possible to identify needs from a global perspective.

[0034] The data collection unit can evaluate the reliability and credibility of data and prioritize the collection of highly reliable information. For example, the data collection unit develops an algorithm to evaluate the reliability of collected data and prioritizes the collection of highly reliable information. For example, it prioritizes information from highly reliable news sites and official blogs. The data collection unit also builds a system to cross-check data from multiple sources to evaluate the credibility of the data. For example, if the same information is provided by multiple highly reliable sources, it prioritizes that information. The data collection unit also evaluates the reliability of collected data in real time and dynamically selects highly reliable information. For example, it evaluates reliability based on user feedback. This allows it to prioritize the collection of highly reliable information.

[0035] The data collection unit can diversify the sources of data collection and integrate information from different perspectives. The data collection unit collects data from different information sources, such as news sites, blogs, and social media, and integrates information from different perspectives. For example, it provides multiple perspectives on the same topic. The data collection unit also diversifies the sources of data collection and provides information that is not biased toward a particular field. For example, it collects information on technology, economics, and culture in a balanced manner. The data collection unit also integrates information from different perspectives and builds a system that provides users with a multifaceted perspective. For example, it provides information that includes different opinions and views. This makes it possible to integrate information from different perspectives.

[0036] The provision unit collects user feedback in real time when a user uses a portal site or smartphone app, allowing the accuracy of information provision to be immediately improved. For example, the provision unit collects user feedback in real time regarding information provided on a portal site or smartphone app, and improves the accuracy of information provision based on the results. For example, it uses an online survey or comment function. The provision unit also analyzes user feedback in real time and identifies areas for improvement in the information to be provided. For example, it adjusts the content of the information based on user opinions. The provision unit also collects user feedback in real time regarding the information to be provided, and develops a system in which the generation AI immediately improves the accuracy of information provision. For example, it automatically corrects the information based on the feedback. This allows feedback to be collected in real time and the accuracy of information provision to be improved.

[0037] The provision unit can use the generation AI to automatically generate information provision specialized for different markets or regions. For example, the provision unit uses the generation AI to build a system that automatically generates information provision specialized for different markets or regions. For example, it provides information according to market needs and regional characteristics. The provision unit also collects data from different markets, and the generation AI provides market-specific information based on that data. For example, it provides information suitable for emerging markets and mature markets. The provision unit also takes into account the culture and economic situation of each region, and the generation AI automatically generates region-specific information. For example, it provides information tailored to local events and trends. This makes it possible to automatically generate information provision specialized for different markets or regions.

[0038] The provision unit can automatically generate visual prototypes of proposed information to make it easier to understand visually. The provision unit, for example, uses generative AI to develop a system that automatically generates visual prototypes of proposed information. For example, it visually displays product designs and service flows. The provision unit also automatically generates visual prototypes to enable users to intuitively understand the content of the information. For example, it provides 3D models and interactive demos. The provision unit also builds a system that generates visual prototypes of proposed information in real time and improves them based on user feedback. For example, it collects user opinions based on the prototype. This makes it possible to automatically generate visual prototypes that are easy to understand visually.

[0039] The providing unit collects feedback from users and, when improving the accuracy of information provision based on that feedback, can automatically classify the content of the feedback and identify areas for improvement. The providing unit, for example, collects feedback from users and automatically classifies the content of the feedback using natural language processing technology. For example, it classifies feedback into positive feedback and negative feedback. The providing unit also analyzes the content of the feedback and builds a system that automatically identifies areas for improvement. For example, it extracts problems pointed out by users and reflects them as areas for improvement. The providing unit also analyzes user feedback in real time and improves the accuracy of information provision based on the content of the feedback. For example, it adjusts the content of the information based on user opinions. In this way, it is possible to automatically classify the content of the feedback and identify areas for improvement.

[0040] The providing unit collects feedback from users and, when improving the accuracy of information provision based on that feedback, can analyze the frequency and trends of the feedback and identify areas for improvement. The providing unit, for example, analyzes the frequency of user feedback and identifies problems that are frequently pointed out. For example, if the same problem is pointed out by multiple users, the problem is improved as a priority. The providing unit also builds a system that analyzes the trends of feedback and identifies common areas for improvement. For example, if there is a lot of feedback on a particular topic, the information related to that topic is improved. The providing unit also analyzes user feedback in real time and improves the accuracy of information provision based on the frequency and trends of feedback. For example, the content of the information is dynamically adjusted based on user opinions. This makes it possible to analyze the frequency and trends of feedback and identify areas for improvement.

[0041] The providing unit can collect feedback from users and, when improving the accuracy of information provision based on that feedback, integrate feedback from different devices and perform comprehensive analysis. The providing unit integrates feedback from different devices, such as smartphones, PCs, and smartwatches, and performs comprehensive analysis. For example, it identifies feedback common to different devices. The providing unit also analyzes feedback from different devices and improves the accuracy of information provision based on the characteristics of each device. For example, it improves health-related information based on feedback from a smartwatch. The providing unit also builds a system that integrates feedback from different devices in real time and performs comprehensive analysis. For example, it adjusts the content of information based on feedback common between devices. This makes it possible to integrate feedback from different devices and perform comprehensive analysis.

[0042] When collecting feedback from users and improving the accuracy of information provision based on that feedback, the provision unit can compare feedback from different languages ​​and cultural spheres and perform analysis from a global perspective. The provision unit, for example, collects feedback in different languages ​​and compares the feedback from each language sphere. For example, it compares feedback from English-speaking countries and Japanese-speaking countries and identifies similarities and differences. The provision unit also analyzes feedback from different cultural spheres and identifies areas for improvement based on cultural background. For example, it adjusts the content of information provision based on feedback from a specific culture. The provision unit also compares feedback from different regions from a global perspective and identifies common areas for improvement. For example, it analyzes feedback for a specific product or service by region. This makes it possible to compare feedback from different languages ​​and cultural spheres and perform analysis from a global perspective.

[0043] When analyzing a user's search history and browsing history, the needs analysis unit can analyze patterns by time period and day of the week to identify the optimal timing for providing information. For example, the needs analysis unit analyzes the user's search history by time period to identify needs during specific time periods. For example, providing relaxation-related information at night. The needs analysis unit can also analyze the browsing history by day of the week to identify needs during specific days of the week. For example, providing leisure-related information on weekends. The needs analysis unit can also integrate the search history and browsing history to analyze patterns by time period and day of the week to identify the optimal timing for providing information. For example, providing news-related information on weekday mornings. This makes it possible to identify the optimal timing for providing information.

[0044] When analyzing a user's search history and browsing history, the needs analysis unit automatically extracts related topics and keywords, allowing the user's interests to be identified in more detail. The needs analysis unit, for example, analyzes a user's search history and automatically extracts related topics and keywords. For example, it identifies interests based on frequently searched keywords. The needs analysis unit also analyzes browsing history and automatically extracts related topics and keywords. For example, it identifies interests based on the content of pages viewed for a long time. The needs analysis unit also integrates search history and browsing history and automatically extracts related topics and keywords to identify interests. For example, it extracts keywords that are common to both searches and browsing. This allows the user's interests to be identified in more detail.

[0045] When analyzing a user's search history and browsing history, the needs analysis unit can integrate data from different devices to perform a more comprehensive analysis. The needs analysis unit, for example, integrates search history from a smartphone and a PC to comprehensively analyze the user's interests. For example, it identifies search keywords that are common across different devices. The needs analysis unit also integrates data from a smartwatch or smart speaker to analyze the user's behavioral patterns. For example, it identifies interests based on voice command history. The needs analysis unit also integrates data from different devices to understand the user's entire life and identify more detailed interests. For example, it analyzes behavioral patterns that are common across devices. This allows the data from different devices to be integrated to perform a comprehensive analysis.

[0046] When analyzing a user's search history or browsing history, the needs analysis unit can compare data from different languages ​​and cultural spheres and perform analysis from a global perspective. For example, the needs analysis unit analyzes search history or browsing history in different languages ​​and compares the interests of users in each language sphere. For example, the needs analysis unit compares the interests of English-speaking and Japanese-speaking users and identifies similarities and differences. The needs analysis unit also analyzes the social media activities of users in different cultural spheres and identifies interests based on cultural background. For example, the needs analysis unit provides information related to holidays in a particular culture. The needs analysis unit also compares the interests of users in different regions from a global perspective and identifies common needs. For example, the needs analysis unit analyzes demand for a particular product or service by region. This allows data from different languages ​​and cultural spheres to be compared and analysis from a global perspective.

[0047] The information generation unit can automatically extract related topics and keywords and generate information tailored to the user's interests. The information generation unit, for example, analyzes the user's search history and browsing history and automatically extracts related topics and keywords. For example, information is generated based on frequently searched keywords. The information generation unit also analyzes browsing history and automatically extracts related topics and keywords. For example, information is generated based on the content of pages viewed for a long time. The information generation unit also integrates search history and browsing history and automatically extracts related topics and keywords to generate information. For example, information is generated based on keywords that are common to both searches and browsing. This makes it possible to generate information tailored to the user's interests.

[0048] The information generation unit can evaluate the reliability and credibility of data and prioritize the generation of highly reliable information. For example, the information generation unit develops an algorithm to evaluate the reliability of collected data and prioritizes the generation of highly reliable information. For example, it prioritizes information from highly reliable news sites and official blogs. The information generation unit also builds a system to cross-check data from multiple information sources to evaluate the credibility of the data. For example, if the same information is provided by multiple highly reliable sources, it prioritizes that information. The information generation unit also evaluates the reliability of collected data in real time and dynamically selects highly reliable information. For example, it evaluates reliability based on user feedback. This allows the generation of highly reliable information to be prioritized.

[0049] The information generation unit can integrate data from different devices to generate more detailed information. For example, the information generation unit analyzes heart rate and activity data from a smartwatch to generate detailed information based on the user's health condition and exercise habits. For example, it provides information about recovery after exercise. The information generation unit also analyzes the voice command history from a smart speaker to generate detailed information based on the user's interests. For example, it provides information about favorite genres based on music playback history. The information generation unit also integrates data from different devices to generate more detailed information by understanding the user's entire lifestyle. For example, it identifies household needs based on data from smart home devices. This allows the information generation unit to integrate data from different devices and generate more detailed information.

[0050] The information generation unit can collect data from different languages ​​and cultures and generate information from a global perspective. For example, the information generation unit collects news articles and blogs in different languages ​​and integrates information from each language to generate information from a global perspective. For example, it compares news from English-speaking and Japanese-speaking countries and identifies similarities and differences. The information generation unit also collects social media posts from different cultures and generates information based on cultural background. For example, it provides information related to holidays in a particular culture. The information generation unit also collects information from different regions from a global perspective and identifies common topics and trends. For example, it analyzes demand for a particular product or service by region. This allows it to generate information from a global perspective.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The needs analysis unit can analyze the user's health data and provide information based on the user's health condition. For example, it can analyze heart rate and sleep data obtained from the smartwatch to understand the user's health condition. This can provide information on relaxation if the user is feeling stressed. Also, if a lack of exercise is detected, it can provide advice on exercise and information on nearby gyms. It can also send reminders for regular health checks.

[0053] The needs analysis unit can analyze a user's purchasing history and provide information based on purchasing trends. For example, it can analyze data on past purchases and suggest new products that the user might be interested in. It can also understand purchasing trends for specific brands or categories and provide information related to those brands or categories. It can also increase the user's desire to purchase by providing sale information and coupons.

[0054] The needs analysis unit can provide information based on the user's hobbies and preferences. For example, if the user is interested in music or movies, the unit can provide the latest release information and reviews related to that genre. If the user likes to travel, the unit can provide recommended spots in travel destinations and information on local events. Furthermore, by providing information on communities and forums related to the user's hobbies, the unit can promote interaction with people who share the same hobbies.

[0055] The needs analysis unit can analyze a user's learning history and provide information based on their learning needs. For example, it can analyze the user's online course history and suggest new courses that might interest the user. It can also track the user's learning progress in a specific field and provide reference books and articles related to that field. Furthermore, it can suggest a study plan based on the user's learning goals and periodically check the user's progress, thereby maintaining the user's motivation to learn.

[0056] The needs analysis unit can analyze a user's fitness data and provide a personalized training plan. For example, it can analyze data obtained from a smartwatch or fitness tracker to understand the user's exercise habits and physical fitness level. This can then be used to suggest the optimal training plan for the user. It can also provide training menus tailored to specific goals (e.g., weight loss or muscle building). Furthermore, it can monitor training progress in real time and adjust the plan as needed.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The needs analysis unit analyzes the user's needs. For example, it analyzes information entered by the user, past search history, and browsing history to identify the user's interests. It can also analyze the user's behavioral patterns to predict potential needs. Step 2: The data collection unit collects data from online sources based on the needs analyzed by the needs analysis unit. For example, data is collected in real time from news sites, blogs, social media, etc., and the collected data is evaluated for its reliability and credibility, allowing for the collection of highly reliable information to be prioritized. Step 3: The information generation unit generates information based on the data collected by the data collection unit. For example, the information generation unit analyzes the collected data and extracts and provides information that is useful to the user. In addition, the emotion estimation function can be used to analyze the user's emotions regarding the collected data and generate information based on the emotions. Step 4: The provision unit provides the information generated by the information generation unit through a portal site or smartphone app. For example, the generated information can be provided to users in real time, and feedback from users can be collected and used to improve the accuracy of the information provided.

[0059] (Example 2) The information provision system according to the embodiment of the present invention is a system that generates personal, niche, and customized summary information of data that users "want to know" in real time according to their individual needs, and provides it as a portal site or smartphone app. As a result, the information provision system can provide a "fountain of knowledge" that can reach all the information that users "want to know."

[0060] An information provision system according to an embodiment includes a needs analysis unit, a data collection unit, an information generation unit, and a provision unit. The needs analysis unit analyzes user needs. For example, the needs analysis unit analyzes information input by the user, past search history, and browsing history to identify the user's interests. The needs analysis unit can also analyze the user's behavioral patterns and predict potential needs. The data collection unit collects data from online information sources based on the needs analyzed by the needs analysis unit. For example, the data collection unit collects data in real time from news sites, blogs, social networking sites, etc. The data collection unit can also evaluate the reliability and credibility of the collected data and prioritize collection of highly reliable information. The information generation unit generates information based on the data collected by the data collection unit. For example, the information generation unit analyzes the collected data and extracts and provides information useful to the user. The information generation unit can also use an emotion estimation function to analyze the user's emotions regarding the collected data and generate information based on the emotions. The provision unit provides the information generated by the information generation unit through a portal site or a smartphone app. For example, the providing unit provides the generated information to the user in real time. The providing unit can also collect feedback from the user and improve the accuracy of the information provided based on the feedback. As a result, the information providing system according to the embodiment can provide information that meets the needs of the user in real time.

[0061] The needs analysis unit can use the user's emotion estimation function to analyze the emotion of the information input by the user and identify needs based on the emotion. For example, the needs analysis unit calculates an emotion score for the information input by the user using the emotion estimation function and identifies needs based on the score. For example, it prioritizes analysis of information with a strong positive emotion. The needs analysis unit also analyzes the emotion of the user's input information in real time and dynamically identifies needs according to changes in emotion. For example, if the user is excited, it provides information related to that excitement. The needs analysis unit also uses the emotion estimation function to identify the type of emotion the user has in the information input by the user and classifies needs based on that emotion. For example, it prioritizes analysis of information with emotions such as joy and surprise. This makes it possible to identify needs based on the user's emotion.

[0062] The needs analysis unit can predict potential needs by analyzing a user's lifestyle patterns and behavioral history. The needs analysis unit, for example, analyzes a user's lifestyle patterns and predicts potential needs based on behavioral history during specific times of the day or day of the week. For example, if a user has the habit of checking the news on weekday mornings, information related to that time period is provided. The needs analysis unit also analyzes a user's behavioral history and predicts future needs from past behavioral patterns. For example, if a user has a history of participating in a specific event, information related to that event is provided. The needs analysis unit also predicts needs according to changes in seasons and weather based on the user's lifestyle patterns. For example, information about heating appliances is provided in winter. This makes it possible to predict the user's potential needs.

[0063] The needs analysis unit can analyze a user's social media activity and track changes in interests in real time. For example, the needs analysis unit analyzes a user's social media posts and identifies changes in interests from the content of the posts and hashtags. For example, if there is an increase in posts about a new hobby, information related to that hobby is provided. The needs analysis unit also analyzes the user's following and like history on social media to track changes in interests. For example, if a user follows a new account, information related to that account is provided. The needs analysis unit also monitors a user's social media activity in real time and dynamically adjusts the information provided in accordance with changes in interests. For example, if there is an increase in posts about a particular topic, information related to that topic is provided. This makes it possible to track changes in a user's interests in real time.

[0064] The needs analysis unit can integrate data from different devices to identify more detailed needs. For example, the needs analysis unit analyzes heart rate and activity data from a smartwatch to identify needs based on the user's health condition and exercise habits. For example, it provides information about recovery after exercise. The needs analysis unit also analyzes the voice command history from a smart speaker to identify the user's interests. For example, it provides information about favorite genres from music playback history. The needs analysis unit also integrates data from different devices to understand the user's entire life and identify more detailed needs. For example, it identifies household needs based on data from smart home devices. This makes it possible to integrate data from different devices to identify detailed needs.

[0065] The needs analysis unit can compare the needs of users from different languages ​​and cultural regions and identify needs from a global perspective. For example, the needs analysis unit analyzes search histories and browsing histories in different languages ​​to compare the needs of users from each language region. For example, it compares the needs of users from English-speaking and Japanese-speaking regions and identifies similarities and differences. The needs analysis unit also analyzes the social media activities of users from different cultural regions and identifies needs based on cultural background. For example, it provides information related to holidays in a particular culture. The needs analysis unit also compares the needs of users from different regions from a global perspective and identifies common needs. For example, it analyzes demand for a particular product or service by region. This makes it possible to identify needs from a global perspective.

[0066] The needs analysis unit uses the emotion estimation function to analyze the emotion a user feels toward specific information and can provide information based on the emotion. The needs analysis unit, for example, analyzes the emotion a user feels toward specific information and provides information based on that emotion. For example, it prioritizes providing information that evokes positive emotions. Furthermore, the needs analysis unit uses the emotion estimation function to adjust to avoid information that the user feels negatively toward when that information is viewed. For example, it does not provide information that causes stress. Furthermore, the needs analysis unit analyzes the user's emotional response in real time and dynamically provides information based on the emotion. For example, if the user is excited, it provides information related to that excitement. This makes it possible to provide information based on the user's emotion.

[0067] The data collection unit can use the emotion estimation function to analyze the user's emotion toward the collected data and generate information based on the emotion. The data collection unit, for example, uses the emotion estimation function on data collected in real time to calculate the user's emotion score. For example, the data collection unit analyzes the user's emotional response to a news article and generates information that elicits positive emotions. The data collection unit also analyzes the user's emotion toward the collected data in real time and dynamically generates information based on the emotion. For example, if the user is excited, information related to that excitement is provided. The data collection unit also uses the emotion estimation function to identify the type of emotion the user has toward the collected data and generate information based on that emotion. For example, information containing emotions such as joy and surprise is preferentially generated. This makes it possible to generate information based on the user's emotion.

[0068] The data collection unit can evaluate the reliability and credibility of data and prioritize the collection of highly reliable information. For example, the data collection unit develops an algorithm to evaluate the reliability of collected data and prioritizes the collection of highly reliable information. For example, it prioritizes information from highly reliable news sites and official blogs. The data collection unit also builds a system to cross-check data from multiple sources to evaluate the credibility of the data. For example, if the same information is provided by multiple highly reliable sources, it prioritizes that information. The data collection unit also evaluates the reliability of collected data in real time and dynamically selects highly reliable information. For example, it evaluates reliability based on user feedback. This allows it to prioritize the collection of highly reliable information.

[0069] The data collection unit can diversify the sources of data collection and integrate information from different perspectives. The data collection unit collects data from different information sources, such as news sites, blogs, and social media, and integrates information from different perspectives. For example, it provides multiple perspectives on the same topic. The data collection unit also diversifies the sources of data collection and provides information that is not biased toward a particular field. For example, it collects information on technology, economics, and culture in a balanced manner. The data collection unit also integrates information from different perspectives and builds a system that provides users with a multifaceted perspective. For example, it provides information that includes different opinions and views. This makes it possible to integrate information from different perspectives.

[0070] The information generation unit can use the emotion estimation function to analyze the user's emotion toward the collected data and generate information based on the emotion. The information generation unit, for example, uses the emotion estimation function on data collected in real time to calculate the user's emotion score. For example, the information generation unit analyzes the user's emotional response to a news article and generates information that elicits positive emotions. The information generation unit also analyzes the user's emotion toward the collected data in real time and dynamically generates information based on the emotion. For example, if the user is excited, information related to that excitement is provided. The information generation unit also uses the emotion estimation function to identify the type of emotion the user has toward the collected data and generate information based on that emotion. For example, information containing emotions such as joy and surprise is preferentially generated. This makes it possible to generate information based on the user's emotion.

[0071] The providing unit can use the emotion estimation function to preferentially display information that evokes the most positive emotions in the user. The providing unit uses the emotion estimation function on information displayed on, for example, a portal site or a smartphone app, and preferentially displays information that evokes the most positive emotions in the user. For example, information with a high emotion score is displayed at the top. The providing unit also analyzes the user's emotional response in real time and dynamically displays information that elicits positive emotions. For example, information that makes the user feel joy is displayed at the top. The providing unit also uses the emotion estimation function to preferentially display information that evokes positive emotions in the user when the user evokes positive emotions in specific information. For example, news that excites the user is displayed at the top. This allows information that evokes the most positive emotions in the user to be displayed at the top.

[0072] The provision unit collects user feedback in real time when a user uses a portal site or smartphone app, allowing the accuracy of information provision to be immediately improved. For example, the provision unit collects user feedback in real time regarding information provided on a portal site or smartphone app, and improves the accuracy of information provision based on the results. For example, it uses an online survey or comment function. The provision unit also analyzes user feedback in real time and identifies areas for improvement in the information to be provided. For example, it adjusts the content of the information based on user opinions. The provision unit also collects user feedback in real time regarding the information to be provided, and develops a system in which the generation AI immediately improves the accuracy of information provision. For example, it automatically corrects the information based on the feedback. This allows feedback to be collected in real time and the accuracy of information provision to be improved.

[0073] The providing unit can use the emotion estimation function to provide personalized information based on the user's emotions. The providing unit, for example, uses the emotion estimation function to build a system that provides personalized information based on the user's emotions. For example, optimal information is provided based on the user's emotion score. The providing unit also analyzes the user's emotional response in real time and provides personalized information based on the results. For example, themes with strong positive emotions are provided preferentially. The providing unit also develops a system that provides information tailored to the user's preferences and interests based on the emotion estimation data. For example, the information content is dynamically adjusted according to changes in the user's emotions. This makes it possible to provide personalized information based on the user's emotions.

[0074] The provision unit can use the generation AI to automatically generate information provision specialized for different markets or regions. For example, the provision unit uses the generation AI to build a system that automatically generates information provision specialized for different markets or regions. For example, it provides information according to market needs and regional characteristics. The provision unit also collects data from different markets, and the generation AI provides market-specific information based on that data. For example, it provides information suitable for emerging markets and mature markets. The provision unit also takes into account the culture and economic situation of each region, and the generation AI automatically generates region-specific information. For example, it provides information tailored to local events and trends. This makes it possible to automatically generate information provision specialized for different markets or regions.

[0075] The provision unit can automatically generate visual prototypes of proposed information to make it easier to understand visually. The provision unit, for example, uses generative AI to develop a system that automatically generates visual prototypes of proposed information. For example, it visually displays product designs and service flows. The provision unit also automatically generates visual prototypes to enable users to intuitively understand the content of the information. For example, it provides 3D models and interactive demos. The provision unit also builds a system that generates visual prototypes of proposed information in real time and improves them based on user feedback. For example, it collects user opinions based on the prototype. This makes it possible to automatically generate visual prototypes that are easy to understand visually.

[0076] The providing unit uses the emotion estimation function to monitor the user's emotional response to new information provided, and can continuously improve the quality of the information provided. The providing unit, for example, uses the emotion estimation function to develop a system that monitors the user's emotional response to new information provided in real time. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. The providing unit also builds a system that continuously improves the quality of information provided based on the user's emotional response data. For example, the system provides information with a high number of positive emotional responses preferentially. The providing unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the quality of information provided. For example, the information content is modified in accordance with changes in the user's emotions. This makes it possible to monitor the user's emotional response to new information provided, and continuously improve the quality of information provided.

[0077] The providing unit can use the emotion estimation function to analyze feedback based on the user's emotions when collecting feedback from the user and improving the accuracy of information provision based on the feedback. The providing unit, for example, uses the emotion estimation function on the feedback from the user to calculate an emotion score. For example, feedback with a positive emotion is preferentially analyzed. The providing unit also analyzes the user's feedback in real time and dynamically reflects emotion-based feedback. For example, feedback that makes the user feel happy is preferentially reflected. The providing unit also uses the emotion estimation function to identify the type of emotion the user has in response to the feedback and classify the feedback based on the emotion. For example, feedback with an emotion of joy or surprise is preferentially analyzed. This makes it possible to analyze feedback based on the user's emotions and improve the accuracy of information provision.

[0078] The providing unit collects feedback from users and, when improving the accuracy of information provision based on that feedback, can automatically classify the content of the feedback and identify areas for improvement. The providing unit, for example, collects feedback from users and automatically classifies the content of the feedback using natural language processing technology. For example, it classifies feedback into positive feedback and negative feedback. The providing unit also analyzes the content of the feedback and builds a system that automatically identifies areas for improvement. For example, it extracts problems pointed out by users and reflects them as areas for improvement. The providing unit also analyzes user feedback in real time and improves the accuracy of information provision based on the content of the feedback. For example, it adjusts the content of the information based on user opinions. In this way, it is possible to automatically classify the content of the feedback and identify areas for improvement.

[0079] The providing unit collects feedback from users and, when improving the accuracy of information provision based on that feedback, can analyze the frequency and trends of the feedback and identify areas for improvement. The providing unit, for example, analyzes the frequency of user feedback and identifies problems that are frequently pointed out. For example, if the same problem is pointed out by multiple users, the problem is improved as a priority. The providing unit also builds a system that analyzes the trends of feedback and identifies common areas for improvement. For example, if there is a lot of feedback on a particular topic, the information related to that topic is improved. The providing unit also analyzes user feedback in real time and improves the accuracy of information provision based on the frequency and trends of feedback. For example, the content of the information is dynamically adjusted based on user opinions. This makes it possible to analyze the frequency and trends of feedback and identify areas for improvement.

[0080] The providing unit can collect feedback from users and, when improving the accuracy of information provision based on that feedback, integrate feedback from different devices and perform comprehensive analysis. The providing unit integrates feedback from different devices, such as smartphones, PCs, and smartwatches, and performs comprehensive analysis. For example, it identifies feedback common to different devices. The providing unit also analyzes feedback from different devices and improves the accuracy of information provision based on the characteristics of each device. For example, it improves health-related information based on feedback from a smartwatch. The providing unit also builds a system that integrates feedback from different devices in real time and performs comprehensive analysis. For example, it adjusts the content of information based on feedback common between devices. This makes it possible to integrate feedback from different devices and perform comprehensive analysis.

[0081] When collecting feedback from users and improving the accuracy of information provision based on that feedback, the provision unit can compare feedback from different languages ​​and cultural spheres and perform analysis from a global perspective. The provision unit, for example, collects feedback in different languages ​​and compares the feedback from each language sphere. For example, it compares feedback from English-speaking countries and Japanese-speaking countries and identifies similarities and differences. The provision unit also analyzes feedback from different cultural spheres and identifies areas for improvement based on cultural background. For example, it adjusts the content of information provision based on feedback from a specific culture. The provision unit also compares feedback from different regions from a global perspective and identifies common areas for improvement. For example, it analyzes feedback for a specific product or service by region. This makes it possible to compare feedback from different languages ​​and cultural spheres and perform analysis from a global perspective.

[0082] The providing unit can use the emotion estimation function to analyze feedback based on the user's emotions when collecting feedback from the user and improving the accuracy of information provision based on the feedback. The providing unit, for example, uses the emotion estimation function on the feedback from the user to calculate an emotion score. For example, feedback with a positive emotion is preferentially analyzed. The providing unit also analyzes the user's feedback in real time and dynamically reflects emotion-based feedback. For example, feedback that makes the user feel happy is preferentially reflected. The providing unit also uses the emotion estimation function to identify the type of emotion the user has in response to the feedback and classify the feedback based on the emotion. For example, feedback with an emotion of joy or surprise is preferentially analyzed. This makes it possible to analyze feedback based on the user's emotions and improve the accuracy of information provision.

[0083] When analyzing a user's past search history and browsing history, the needs analysis unit can use the emotion estimation function to identify which information the user feels positive about. The needs analysis unit, for example, analyzes the user's past search history and uses the emotion estimation function to identify information that evokes positive emotions. For example, it extracts information that evokes joy or excitement from the search history. The needs analysis unit also analyzes the browsing history and uses the emotion estimation function to identify pages and content that evoke positive emotions. For example, it prioritizes the analysis of pages that have been viewed for a long time. The needs analysis unit also integrates the search history and browsing history and uses the emotion estimation function to comprehensively identify information that evokes positive emotions. For example, it extracts information that shows a positive reaction in both searching and browsing. This makes it possible to identify information that evokes positive emotions in the user.

[0084] When analyzing a user's search history and browsing history, the needs analysis unit can analyze patterns by time period and day of the week to identify the optimal timing for providing information. For example, the needs analysis unit analyzes the user's search history by time period to identify needs during specific time periods. For example, providing relaxation-related information at night. The needs analysis unit can also analyze the browsing history by day of the week to identify needs during specific days of the week. For example, providing leisure-related information on weekends. The needs analysis unit can also integrate the search history and browsing history to analyze patterns by time period and day of the week to identify the optimal timing for providing information. For example, providing news-related information on weekday mornings. This makes it possible to identify the optimal timing for providing information.

[0085] When analyzing a user's search history and browsing history, the needs analysis unit automatically extracts related topics and keywords, allowing the user's interests to be identified in more detail. The needs analysis unit, for example, analyzes a user's search history and automatically extracts related topics and keywords. For example, it identifies interests based on frequently searched keywords. The needs analysis unit also analyzes browsing history and automatically extracts related topics and keywords. For example, it identifies interests based on the content of pages viewed for a long time. The needs analysis unit also integrates search history and browsing history and automatically extracts related topics and keywords to identify interests. For example, it extracts keywords that are common to both searches and browsing. This allows the user's interests to be identified in more detail.

[0086] When analyzing a user's search history and browsing history, the needs analysis unit can integrate data from different devices to perform a more comprehensive analysis. The needs analysis unit, for example, integrates search history from a smartphone and a PC to comprehensively analyze the user's interests. For example, it identifies search keywords that are common across different devices. The needs analysis unit also integrates data from a smartwatch or smart speaker to analyze the user's behavioral patterns. For example, it identifies interests based on voice command history. The needs analysis unit also integrates data from different devices to understand the user's entire life and identify more detailed interests. For example, it analyzes behavioral patterns that are common across devices. This allows the data from different devices to be integrated to perform a comprehensive analysis.

[0087] When analyzing a user's search history or browsing history, the needs analysis unit can compare data from different languages ​​and cultural spheres and perform analysis from a global perspective. For example, the needs analysis unit analyzes search history or browsing history in different languages ​​and compares the interests of users in each language sphere. For example, the needs analysis unit compares the interests of English-speaking and Japanese-speaking users and identifies similarities and differences. The needs analysis unit also analyzes the social media activities of users in different cultural spheres and identifies interests based on cultural background. For example, the needs analysis unit provides information related to holidays in a particular culture. The needs analysis unit also compares the interests of users in different regions from a global perspective and identifies common needs. For example, the needs analysis unit analyzes demand for a particular product or service by region. This allows data from different languages ​​and cultural spheres to be compared and analysis from a global perspective.

[0088] When analyzing a user's search history and browsing history, the needs analysis unit uses an emotion estimation function to analyze the user's emotions toward specific information, and can provide information based on the emotions. The needs analysis unit, for example, analyzes the user's search history and uses the emotion estimation function to identify information that evokes positive emotions. For example, it extracts information that evokes joy or excitement from the search history. The needs analysis unit also analyzes the browsing history and uses the emotion estimation function to identify pages and content that evoke positive emotions. For example, it prioritizes analyzing pages that have been viewed for a long time. The needs analysis unit also integrates the search history and browsing history and uses the emotion estimation function to comprehensively identify information that evokes positive emotions. For example, it extracts information that shows a positive reaction in both searching and browsing. This makes it possible to provide information based on the user's emotions.

[0089] When generating information according to the user's needs, the information generation unit can use the emotion estimation function to generate information that evokes the user's most positive emotion. The information generation unit, for example, uses the emotion estimation function to generate information that evokes the user's most positive emotion. For example, information with a high emotion score is preferentially provided. The information generation unit also analyzes the user's emotional response in real time and dynamically generates information that elicits positive emotion. For example, information that evokes joy is provided. The information generation unit also uses the emotion estimation function to preferentially generate information that evokes positive emotion in the user when the user feels positive emotion toward that information. For example, news that excites the user is provided. This makes it possible to generate information that evokes the user's most positive emotion.

[0090] The information generation unit can automatically extract related topics and keywords and generate information tailored to the user's interests. The information generation unit, for example, analyzes the user's search history and browsing history and automatically extracts related topics and keywords. For example, information is generated based on frequently searched keywords. The information generation unit also analyzes browsing history and automatically extracts related topics and keywords. For example, information is generated based on the content of pages viewed for a long time. The information generation unit also integrates search history and browsing history and automatically extracts related topics and keywords to generate information. For example, information is generated based on keywords that are common to both searches and browsing. This makes it possible to generate information tailored to the user's interests.

[0091] The information generation unit can evaluate the reliability and credibility of data and prioritize the generation of highly reliable information. For example, the information generation unit develops an algorithm to evaluate the reliability of collected data and prioritizes the generation of highly reliable information. For example, it prioritizes information from highly reliable news sites and official blogs. The information generation unit also builds a system to cross-check data from multiple information sources to evaluate the credibility of the data. For example, if the same information is provided by multiple highly reliable sources, it prioritizes that information. The information generation unit also evaluates the reliability of collected data in real time and dynamically selects highly reliable information. For example, it evaluates reliability based on user feedback. This allows the generation of highly reliable information to be prioritized.

[0092] The information generation unit can integrate data from different devices to generate more detailed information. For example, the information generation unit analyzes heart rate and activity data from a smartwatch to generate detailed information based on the user's health condition and exercise habits. For example, it provides information about recovery after exercise. The information generation unit also analyzes the voice command history from a smart speaker to generate detailed information based on the user's interests. For example, it provides information about favorite genres based on music playback history. The information generation unit also integrates data from different devices to generate more detailed information by understanding the user's entire lifestyle. For example, it identifies household needs based on data from smart home devices. This allows the information generation unit to integrate data from different devices and generate more detailed information.

[0093] The information generation unit can collect data from different languages ​​and cultures and generate information from a global perspective. For example, the information generation unit collects news articles and blogs in different languages ​​and integrates information from each language to generate information from a global perspective. For example, it compares news from English-speaking and Japanese-speaking countries and identifies similarities and differences. The information generation unit also collects social media posts from different cultures and generates information based on cultural background. For example, it provides information related to holidays in a particular culture. The information generation unit also collects information from different regions from a global perspective and identifies common topics and trends. For example, it analyzes demand for a particular product or service by region. This allows it to generate information from a global perspective.

[0094] The information generation unit can use the emotion estimation function to analyze the user's emotion toward the collected data and generate information based on the emotion. The information generation unit, for example, collects news articles and blogs on the Internet and calculates the user's emotion score using the emotion estimation function. For example, information that elicits positive emotions is preferentially provided. The information generation unit also analyzes the user's emotion toward the collected data in real time and dynamically provides information based on the emotion. For example, if the user is excited, information related to that excitement is provided. The information generation unit also uses the emotion estimation function to identify the type of emotion the user has toward the collected data and provides information based on that emotion. For example, information that conveys emotions such as joy and surprise is preferentially provided. This makes it possible to generate information based on the user's emotion.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The needs analysis unit can analyze the user's health data and provide information based on the user's health condition. For example, it can analyze heart rate and sleep data obtained from the smartwatch to understand the user's health condition. This can provide information on relaxation if the user is feeling stressed. Also, if a lack of exercise is detected, it can provide advice on exercise and information on nearby gyms. It can also send reminders for regular health checks.

[0097] The needs analysis unit can analyze a user's purchasing history and provide information based on purchasing trends. For example, it can analyze data on past purchases and suggest new products that the user might be interested in. It can also understand purchasing trends for specific brands or categories and provide information related to those brands or categories. It can also increase the user's desire to purchase by providing sale information and coupons.

[0098] The needs analysis unit can provide information based on the user's hobbies and preferences. For example, if the user is interested in music or movies, the unit can provide the latest release information and reviews related to that genre. If the user likes to travel, the unit can provide recommended spots in travel destinations and information on local events. Furthermore, by providing information on communities and forums related to the user's hobbies, the unit can promote interaction with people who share the same hobbies.

[0099] The needs analysis unit can analyze a user's learning history and provide information based on their learning needs. For example, it can analyze the user's online course history and suggest new courses that might interest the user. It can also track the user's learning progress in a specific field and provide reference books and articles related to that field. Furthermore, it can suggest a study plan based on the user's learning goals and periodically check the user's progress, thereby maintaining the user's motivation to learn.

[0100] The needs analysis unit can analyze a user's fitness data and provide a personalized training plan. For example, it can analyze data obtained from a smartwatch or fitness tracker to understand the user's exercise habits and physical fitness level. This can then be used to suggest the optimal training plan for the user. It can also provide training menus tailored to specific goals (e.g., weight loss or muscle building). Furthermore, it can monitor training progress in real time and adjust the plan as needed.

[0101] The needs analysis unit uses the user's emotion estimation function to analyze the user's emotions toward specific information and provide information based on those emotions. For example, if the user has positive emotions toward a specific news article, information related to that article can be provided preferentially. Also, if the user has negative emotions, information to alleviate those emotions can be provided. Furthermore, it is possible to monitor changes in the user's emotions in real time and dynamically adjust the information provided according to emotions.

[0102] The data collection unit can use the emotion estimation function to analyze the user's emotions regarding the collected data and generate emotion-based information. For example, it can analyze the user's emotional response to a news article and provide information that elicits positive emotions preferentially. It can also provide information to alleviate negative emotions if the user is feeling negative. It can also monitor changes in the user's emotions in real time and dynamically adjust the information provided based on emotions.

[0103] The information generation unit can use the emotion estimation function to analyze the user's emotions toward the collected data and generate information based on those emotions. For example, it can analyze the user's emotional response to a news article and provide information that elicits positive emotions preferentially. It can also provide information to alleviate negative emotions if the user is feeling negative. It can also monitor changes in the user's emotions in real time and dynamically adjust the information provided based on emotions.

[0104] The providing unit can use the emotion estimation function to preferentially display information that evokes the most positive emotions in the user. For example, the emotion estimation function can be used for information displayed on a portal site or smartphone app to preferentially display information that evokes the most positive emotions in the user. The providing unit can also analyze the user's emotional response in real time and dynamically display information that elicits positive emotions. Furthermore, if the user evokes positive emotions toward specific information, it can also preferentially display that information.

[0105] The providing unit can use the emotion estimation function to monitor the user's emotional response to new information provided and continuously improve the quality of the information provided. For example, the emotion estimation function can be used to monitor the user's emotional response to new information provided in real time. Also, a system can be built that continuously improves the quality of information provided based on the user's emotional response data. Furthermore, emotion estimation data can be collected in real time and the quality of information provided can be dynamically adjusted.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The needs analysis unit analyzes the user's needs. For example, it analyzes information entered by the user, past search history, and browsing history to identify the user's interests. It can also analyze the user's behavioral patterns to predict potential needs. Step 2: The data collection unit collects data from online sources based on the needs analyzed by the needs analysis unit. For example, data is collected in real time from news sites, blogs, social media, etc., and the collected data is evaluated for its reliability and credibility, allowing for the collection of highly reliable information to be prioritized. Step 3: The information generation unit generates information based on the data collected by the data collection unit. For example, the information generation unit analyzes the collected data and extracts and provides information that is useful to the user. In addition, the emotion estimation function can be used to analyze the user's emotions regarding the collected data and generate information based on the emotions. Step 4: The provision unit provides the information generated by the information generation unit through a portal site or smartphone app. For example, the generated information can be provided to users in real time, and feedback from users can be collected and used to improve the accuracy of the information provided.

[0108] 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.

[0109] 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.

[0110] 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.

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

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

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

[0140] The data processing system 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 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 needs analysis unit that analyzes user needs; a data collection unit that collects data from information sources on the Internet based on the needs analyzed by the needs analysis unit; an information generating unit that generates information based on the data collected by the data collecting unit; a providing unit that provides the information generated by the information generating unit through a portal site or a smartphone app. A system characterized by:

2. The needs analysis unit Analyzing the user's feelings about the information input by the user and identifying the needs based on the feelings.

2. The system of claim 1.

3. The needs analysis unit Analyzing the user's lifestyle patterns and behavioral history to predict potential needs 2. The system of claim 1.

4. The needs analysis unit Analyzing the user's social media activity and tracking changes in interests in real time 2. The system of claim 1.

5. The needs analysis unit Integrate the data from different devices to identify the needs in more detail 2. The system of claim 1.

6. The needs analysis unit Compare the needs of users from different languages ​​and cultures to identify the needs from a global perspective 2. The system of claim 1.

7. The needs analysis unit Analyzing the user's feelings toward the specific information and providing information based on the feelings 2. The system of claim 1.

8. The data collection unit Analyzing the user's feelings regarding the collected data and generating the information based on the feelings.

2. The system of claim 1.

Images