system

Abstract

The system according to the embodiment aims to provide information about destinations in a unified manner and improve convenience. [Solution] A system according to an embodiment includes an information acquisition unit, an analysis unit, a display unit, and a linking unit. The information acquisition unit acquires information from information sources on the Internet. The analysis unit analyzes the information acquired by the information acquisition unit. The display unit displays the information analyzed by the analysis unit on a UI. The linking unit links with an electronic payment system based on the information displayed by the display unit.

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] With conventional technology, when traveling or going out, users had to collect information about their destination from multiple sites and apps, which was a time-consuming process.

[0005] The system according to the embodiment aims to provide information about destinations in a unified manner and improve convenience. [Means for solving the problem]

[0006] The system according to the embodiment includes an information acquisition unit, an analysis unit, a display unit, and a linking unit. The information acquisition unit acquires information from information sources on the Internet. The analysis unit analyzes the information acquired by the information acquisition unit. The display unit displays the information analyzed by the analysis unit on a UI. The linking unit links with an electronic payment system based on the information displayed by the display unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information about destinations in a unified manner, thereby improving convenience. [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 travel information integration system according to an embodiment of the present invention allows users to check information about their destinations on a single UI when traveling or going out. When a user sets a destination, the system retrieves, analyzes, and displays real-time information from online sources. The retrieved information includes weather, temperature, congestion, and restaurant information. This allows users to check the latest information about their destinations on a single system without having to navigate multiple websites or apps. The system can also be linked to an electronic payment system, allowing for smooth payments at stores near the destination. For example, when a user sets "Tokyo Station" as their destination, an information retrieval unit retrieves information about the weather, temperature, congestion, and restaurant information about Tokyo Station from online sources. An analysis unit analyzes this information, and a display unit displays it on a single UI. Users can check the weather, temperature, congestion, and restaurant information at a glance. Furthermore, the linking unit links with an electronic payment system, allowing for smooth payments at stores near Tokyo Station. This system allows users to check the latest information about their destinations on a single system without having to navigate multiple websites or apps. Linking with an electronic payment system also allows for smooth payments at stores near the destination. This will enable the travel information integrated system to allow users to check information about their destination on a single UI, and it will also be possible to link with electronic payment systems.

[0029] The travel information integration system according to the embodiment includes an information acquisition unit, an analysis unit, a display unit, and a linking unit. The information acquisition unit acquires information from information sources on the Internet. The information acquisition unit can acquire information from, for example, news sites, social networking sites, blogs, etc. The information acquisition unit can acquire information using an API or by scraping. The analysis unit analyzes the information acquired by the information acquisition unit. The analysis unit analyzes the information using, for example, text analysis, image analysis, data mining, or other methods. The analysis unit can classify the acquired information and organize it based on importance or relevance. The display unit displays the information analyzed by the analysis unit in a UI. The display unit can display the information in the form of, for example, a webpage, a mobile app, a dashboard, or the like. The display unit can also visually display the information using graphs or charts to enable a user to intuitively understand the information. The linking unit links with an electronic payment system based on the information displayed by the display unit. The linking unit can link with the electronic payment system using, for example, credit card payment, electronic money, two-dimensional code (e.g., QR Code (registered trademark)) payment, etc. The linking unit manages linking with electronic payment systems so that users can smoothly make payments at stores near their destinations. As a result, the travel information integrated system according to the embodiment allows users to check information about their destinations on a single UI and also enables linking with electronic payment systems.

[0030] The travel information integrated system includes a weather information acquisition unit that acquires weather information. The weather information acquisition unit acquires weather information. The weather information acquisition unit can acquire weather information such as temperature, precipitation, and wind speed. The weather information acquisition unit can acquire weather information from data from the Japan Meteorological Agency or private weather services. The weather information acquisition unit can acquire weather information in real time and provide it to the user. For example, the weather information acquisition unit uses an API to acquire data from the Japan Meteorological Agency and update it in real time. The weather information acquisition unit can also acquire data provided by private weather services and provide it to the user. This allows the weather information acquisition unit to check the weather at the user's destination.

[0031] The travel information integrated system includes a temperature information acquisition unit that acquires temperature information. The temperature information acquisition unit acquires temperature information. The temperature information acquisition unit can acquire temperature information, for example, from data from the Japan Meteorological Agency or private weather services. The temperature information acquisition unit can acquire temperature information in real time and provide it to the user. For example, the temperature information acquisition unit acquires data from the Japan Meteorological Agency using an API and updates it in real time. The temperature information acquisition unit can also acquire data provided by private weather services and provide it to the user. This allows the temperature information acquisition unit to check the temperature at the user's destination.

[0032] The travel information integrated system includes a congestion status acquisition unit that acquires congestion status. The congestion status acquisition unit acquires congestion status. The congestion status acquisition unit can acquire congestion status from, for example, real-time people flow data or past statistical data. The congestion status acquisition unit can acquire congestion status in real time and provide it to the user. For example, the congestion status acquisition unit acquires real-time people flow data using an API and updates congestion status in real time. The congestion status acquisition unit can also predict congestion status based on past statistical data and provide it to the user. This allows the user to check the congestion status at their destination.

[0033] The travel information integrated system includes a gourmet information acquisition unit that acquires gourmet information. The gourmet information acquisition unit acquires gourmet information. The gourmet information acquisition unit can acquire gourmet information from, for example, restaurant review sites or posts on social media. The gourmet information acquisition unit can acquire gourmet information in real time and provide it to the user. For example, the gourmet information acquisition unit uses an API to acquire data from restaurant review sites and update it in real time. The gourmet information acquisition unit can also analyze posts on social media and provide it to the user. This allows the gourmet information acquisition unit to check gourmet information for the user's destination.

[0034] The linking unit links with an electronic payment system to enable payments at stores near the destination. The linking unit links with the electronic payment system. The linking unit can link with the electronic payment system by methods such as credit card payment, electronic money, and two-dimensional code payment. The linking unit manages the link with the electronic payment system so that the user can smoothly make payments at stores near the destination. For example, the linking unit allows the user to make payments through the electronic payment system by scanning a two-dimensional code. The linking unit can also perform authentication through the electronic payment system when the user makes a payment using a credit card. In this way, the linking unit enables the user to smoothly make payments at stores near the destination.

[0035] The information acquisition unit can analyze the user's past search history and select an appropriate information acquisition method. The information acquisition unit analyzes the user's past search history. The past search history can be acquired, for example, from browser history or search engine logs. The information acquisition unit selects the optimal information acquisition method based on the past search history. For example, it prioritizes the use of information sources that the user has frequently searched for in the past. It can also predict the type of information to be acquired during a specific time period from the user's past search history and acquire it in the optimal method. Furthermore, it can analyze the user's past search history and select the most reliable information source. This allows the information acquisition unit to analyze the user's past search history and select the optimal information acquisition method.

[0036] The information acquisition unit can perform filtering based on the user's current areas of interest when acquiring information. The information acquisition unit performs filtering based on the user's current areas of interest when acquiring information. The current areas of interest can be identified, for example, from recent search keywords, browsing history, etc. The information acquisition unit filters unnecessary information based on the user's current areas of interest. For example, only information related to topics in which the user is currently interested can be acquired. Unnecessary information can also be filtered based on the user's current areas of interest. Furthermore, the latest information related to the user's areas of interest can be preferentially acquired. This allows the information acquisition unit to filter information based on the user's current areas of interest and eliminate unnecessary information.

[0037] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. When acquiring information, the information acquisition unit prioritizes acquiring highly relevant information in consideration of the user's geographical location information. The geographical location information can be acquired from, for example, GPS data or an IP address. The information acquisition unit prioritizes acquiring nearby information based on the user's current location. For example, it prioritizes acquiring weather information related to the user's current location. It can also prioritize acquiring nearby congestion information based on the user's current location. Furthermore, it can also prioritize acquiring nearby gourmet information based on the user's geographical location information. This allows the information acquisition unit to prioritize acquiring highly relevant information in consideration of the user's geographical location information.

[0038] The information acquisition unit can analyze the user's social media activity when acquiring information and acquire related information. The information acquisition unit analyzes the user's social media activity when acquiring information. Social media activity can be acquired from, for example, the content of posts, the number of likes, the number of followers, etc. The information acquisition unit acquires related information based on the user's social media activity. For example, information related to places shared by the user on social media is preferentially acquired. Information related to topics of interest to the user can also be acquired from the user's social media activity. Furthermore, related information can be acquired based on the content of posts from accounts the user follows. This allows the information acquisition unit to analyze the user's social media activity and acquire related information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. The importance of the information can be evaluated, for example, based on the user's level of interest or the reliability of the information. The analysis unit performs a detailed analysis of information with high importance. For example, detailed analysis results are provided for information with high importance. Also, a concise analysis can be performed for information with low importance. Furthermore, the analysis priority can be determined according to the importance of the information. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the information and perform efficient analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on the category of information during analysis. Information categories can be classified, for example, into news, entertainment, sports, etc. The analysis unit applies the most appropriate analysis algorithm depending on the category of information. For example, a weather data analysis algorithm can be applied to weather information. A real-time data analysis algorithm can also be applied to congestion status. Furthermore, a word-of-mouth data analysis algorithm can be applied to gourmet information. In this way, the analysis unit can apply different analysis algorithms depending on the category of information, allowing for more accurate analysis.

[0041] The analysis unit can determine the priority of analysis based on the time when the information was acquired during analysis. The analysis unit can determine the priority of analysis based on the time when the information was acquired during analysis. The time when the information was acquired can be evaluated, for example, from real-time data or past data. The analysis unit prioritizes analysis of the most recent information. For example, detailed analysis results can be provided for the most recent information. Also, a concise analysis can be performed for older information. Furthermore, the level of detail of the analysis can be adjusted depending on the time when the information was acquired. This allows the analysis unit to determine the priority of analysis based on the time when the information was acquired, and prioritize analysis of the most recent information.

[0042] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. The relevance of information can be evaluated, for example, based on the user's level of interest or the content of the information. The analysis unit prioritizes analysis of highly relevant information. For example, detailed analysis results are provided for highly relevant information. Also, a concise analysis can be performed for less relevant information. Furthermore, the analysis order can be determined according to the relevance of the information. In this way, the analysis unit can adjust the order of analysis based on the relevance of information and perform efficient analysis.

[0043] The display unit can adjust the level of detail of the display based on the importance of the information when displaying the information. The display unit adjusts the level of detail of the display based on the importance of the information when displaying the information. The importance of the information can be evaluated, for example, based on the user's level of interest or the reliability of the information. The display unit displays information of high importance in detail. For example, detailed information is displayed for information of high importance. Also, information of low importance can be displayed in a concise manner. Furthermore, the display priority can be determined according to the importance of the information. In this way, the display unit can adjust the level of detail of the display based on the importance of the information and provide information efficiently.

[0044] The display unit can apply different display algorithms depending on the category of information when displaying the information. The display unit applies different display algorithms depending on the category of information when displaying the information. Information categories can be classified, for example, into news, entertainment, sports, etc. The display unit applies the most appropriate display algorithm depending on the category of information. For example, a weather data display algorithm can be applied to weather information. A real-time data display algorithm can also be applied to congestion status. Furthermore, a word-of-mouth data display algorithm can also be applied to gourmet information. In this way, the display unit can apply different display algorithms depending on the category of information and provide more accurate information.

[0045] The display unit can determine the display priority based on the time when the information was acquired when it was displayed. The display unit can determine the display priority based on the time when the information was acquired when it was displayed. The time when the information was acquired can be evaluated, for example, from real-time data or past data. The display unit preferentially displays the latest information. For example, the latest information is displayed in detail. Old information can also be displayed in a concise manner. Furthermore, the level of detail of the display can be adjusted depending on the time when the information was acquired. This allows the display unit to determine the display priority based on the time when the information was acquired and preferentially display the latest information.

[0046] The display unit can adjust the display order based on the relevance of the information when displaying the information. The display unit adjusts the display order based on the relevance of the information when displaying the information. The relevance of the information can be evaluated, for example, based on the user's level of interest or the content of the information. The display unit preferentially displays highly relevant information. For example, highly relevant information is displayed in detail. Also, less relevant information can be displayed in a concise manner. Furthermore, the display order can be determined according to the relevance of the information. In this way, the display unit can adjust the display order based on the relevance of the information and provide information efficiently.

[0047] At the time of collaboration, the collaboration unit can analyze the user's past collaboration history and select an appropriate collaboration method. At the time of collaboration, the collaboration unit analyzes the user's past collaboration history. The past collaboration history can be obtained, for example, from a collaboration log or the user's operation history. The collaboration unit selects the optimal collaboration method based on the past collaboration history. For example, the collaboration unit preferentially suggests collaboration methods that the user has used in the past. The collaboration unit can also select the most efficient collaboration method from the user's past collaboration history. Furthermore, the collaboration unit can analyze the user's past collaboration history and suggest the optimal collaboration method. This allows the collaboration unit to analyze the user's past collaboration history and select the optimal collaboration method.

[0048] The collaboration unit can customize the collaboration means based on the user's current living situation at the time of collaboration. The collaboration unit customizes the collaboration means based on the user's current living situation at the time of collaboration. The current living situation can be identified, for example, from the user's schedule or location information. The collaboration unit provides the optimal collaboration means according to the user's living situation. For example, if the user is busy, a quick collaboration means can be provided. Also, if the user is relaxed, a detailed collaboration means can be provided. Furthermore, the collaboration unit can suggest the optimal collaboration means according to the user's living situation. This allows the collaboration unit to customize the collaboration means according to the user's living situation and perform more appropriate collaboration.

[0049] The collaboration unit can select an appropriate collaboration method in consideration of the user's geographical location information when collaborating. The collaboration unit selects the optimal collaboration method in consideration of the user's geographical location information when collaborating. Geographical location information can be acquired from, for example, GPS data or an IP address. The collaboration unit provides the optimal collaboration method based on the user's current location. For example, the collaboration unit can suggest the optimal collaboration method based on the user's current location. It can also provide a collaboration method with nearby stores based on the user's geographical location information. Furthermore, it can select the most efficient collaboration method according to the user's current location. This allows the collaboration unit to select the optimal collaboration method in consideration of the user's geographical location information.

[0050] The collaboration unit can analyze the user's social media activity at the time of collaboration and suggest collaboration methods. The collaboration unit analyzes the user's social media activity at the time of collaboration. Social media activity can be obtained, for example, from the content of posts, the number of likes, the number of followers, etc. The collaboration unit suggests the optimal collaboration method based on the user's social media activity. For example, the collaboration unit can suggest collaboration methods related to places the user has shared on social media. The collaboration unit can also provide collaboration methods with stores of interest based on the user's social media activity. Furthermore, the collaboration unit can suggest the optimal collaboration method based on the content of posts from accounts the user follows. This allows the collaboration unit to analyze the user's social media activity and suggest the optimal collaboration method.

[0051] When acquiring weather information, the weather information acquisition unit can analyze the user's past weather information usage history and select an appropriate acquisition method. When acquiring weather information, the weather information acquisition unit analyzes the user's past weather information usage history. The past weather information usage history can be acquired, for example, from a weather app usage history or search history. The weather information acquisition unit selects the optimal acquisition method based on the past weather information usage history. For example, it prioritizes the use of weather information sources that the user has frequently used in the past. It can also predict the type of weather information to be acquired during a specific time period from the user's past weather information usage history and acquire it using the optimal method. It can also analyze the user's past weather information usage history and select the most reliable weather information source. This allows the weather information acquisition unit to analyze the user's past weather information usage history and select the optimal acquisition method.

[0052] The weather information acquisition unit can filter the weather information based on the user's current areas of interest when acquiring the weather information. The weather information acquisition unit can filter the weather information based on the user's current areas of interest when acquiring the weather information. The current areas of interest can be identified, for example, from recent search keywords or browsing history. The weather information acquisition unit filters unnecessary weather information based on the user's current areas of interest. For example, only information related to weather in which the user is currently interested can be acquired. Unnecessary weather information can also be filtered based on the user's current areas of interest. Furthermore, the latest weather information related to the user's areas of interest can be preferentially acquired. This allows the weather information acquisition unit to filter weather information based on the user's current areas of interest and eliminate unnecessary information.

[0053] When acquiring weather information, the weather information acquisition unit can prioritize acquiring highly relevant weather information taking into account the user's geographical location information. When acquiring weather information, the weather information acquisition unit prioritizes acquiring highly relevant weather information taking into account the user's geographical location information. Geographical location information can be acquired from, for example, GPS data or an IP address. The weather information acquisition unit prioritizes acquiring nearby weather information based on the user's current location. For example, it prioritizes acquiring weather information related to the user's current location. It can also prioritize acquiring nearby weather information based on the user's current location. Furthermore, it can also prioritize acquiring nearby weather information based on the user's geographical location information. This allows the weather information acquisition unit to prioritize acquiring highly relevant weather information taking into account the user's geographical location information.

[0054] When acquiring temperature information, the temperature information acquisition unit can analyze the user's past temperature information usage history and select an appropriate acquisition method. When acquiring temperature information, the temperature information acquisition unit analyzes the user's past temperature information usage history. The past temperature information usage history can be acquired, for example, from the temperature app usage history or search history. The temperature information acquisition unit selects the optimal acquisition method based on the past temperature information usage history. For example, it prioritizes the use of temperature information sources that the user has frequently used in the past. It can also predict the type of temperature information to be acquired during a specific time period from the user's past temperature information usage history and acquire it using the optimal method. It can also analyze the user's past temperature information usage history and select the most reliable temperature information source. In this way, the temperature information acquisition unit can analyze the user's past temperature information usage history and select the optimal acquisition method.

[0055] The temperature information acquisition unit can perform filtering based on the user's current areas of interest when acquiring temperature information. The temperature information acquisition unit performs filtering based on the user's current areas of interest when acquiring temperature information. The current areas of interest can be identified, for example, from recent search keywords or browsing history. The temperature information acquisition unit filters unnecessary temperature information based on the user's current areas of interest. For example, only information related to temperatures in which the user is currently interested can be acquired. Unnecessary temperature information can also be filtered based on the user's current areas of interest. Furthermore, the latest temperature information related to the user's areas of interest can be preferentially acquired. This allows the temperature information acquisition unit to filter temperature information based on the user's current areas of interest and eliminate unnecessary information.

[0056] When acquiring temperature information, the temperature information acquisition unit can prioritize acquiring highly relevant temperature information in consideration of the user's geographical location information. When acquiring temperature information, the temperature information acquisition unit prioritizes acquiring highly relevant temperature information in consideration of the user's geographical location information. Geographical location information can be acquired from, for example, GPS data or an IP address. The temperature information acquisition unit prioritizes acquiring nearby temperature information based on the user's current location. For example, it prioritizes acquiring temperature information related to the user's current location. It can also prioritize acquiring nearby temperature information based on the user's current location. Furthermore, it can also prioritize acquiring nearby temperature information based on the user's geographical location information. This allows the temperature information acquisition unit to prioritize acquiring highly relevant temperature information in consideration of the user's geographical location information.

[0057] When acquiring congestion status, the congestion status acquisition unit can analyze the user's past congestion status usage history and select an appropriate acquisition method. When acquiring congestion status, the congestion status acquisition unit analyzes the user's past congestion status usage history. The past congestion status usage history can be acquired, for example, from the usage history or search history of a congestion status app. The congestion status acquisition unit selects the optimal acquisition method based on the past congestion status usage history. For example, the congestion status acquisition unit prioritizes the use of congestion status information sources that the user has frequently used in the past. In addition, the type of congestion status to be acquired during a specific time period can be predicted from the user's past congestion status usage history and acquired using the optimal method. Furthermore, the user's past congestion status usage history can be analyzed to select the most reliable congestion status information source. In this way, the congestion status acquisition unit can analyze the user's past congestion status usage history and select the optimal acquisition method.

[0058] The congestion status acquisition unit can perform filtering based on the user's current area of ​​interest when acquiring the congestion status. The congestion status acquisition unit performs filtering based on the user's current area of ​​interest when acquiring the congestion status. The current area of ​​interest can be identified, for example, from recent search keywords or browsing history. The congestion status acquisition unit filters unnecessary congestion status information based on the user's current area of ​​interest. For example, only information related to congestion status that the user is currently interested in can be acquired. In addition, unnecessary congestion status information can be filtered based on the user's current area of ​​interest. Furthermore, the latest congestion status information related to the user's area of ​​interest can be preferentially acquired. In this way, the congestion status acquisition unit can filter congestion status information based on the user's current area of ​​interest and eliminate unnecessary information.

[0059] When acquiring congestion status, the congestion status acquisition unit can prioritize acquiring highly relevant congestion status in consideration of the user's geographical location information. When acquiring congestion status, the congestion status acquisition unit prioritizes acquiring highly relevant congestion status in consideration of the user's geographical location information. The geographical location information can be acquired from, for example, GPS data or an IP address. The congestion status acquisition unit prioritizes acquiring nearby congestion status based on the user's current location. For example, congestion status information related to the user's current location can be prioritized. Also, nearby congestion status information can be prioritized based on the user's current location. Furthermore, nearby congestion status information can be prioritized based on the user's geographical location information. This allows the congestion status acquisition unit to prioritize acquiring highly relevant congestion status in consideration of the user's geographical location information.

[0060] When acquiring gourmet information, the gourmet information acquisition unit can analyze the user's past gourmet information usage history and select an appropriate acquisition method. When acquiring gourmet information, the gourmet information acquisition unit analyzes the user's past gourmet information usage history. The past gourmet information usage history can be acquired, for example, from a gourmet app usage history or a search history. The gourmet information acquisition unit selects the optimal acquisition method based on the past gourmet information usage history. For example, it prioritizes the use of gourmet information sources that the user has frequently used in the past. It is also possible to predict the type of gourmet information to be acquired during a specific time period from the user's past gourmet information usage history and acquire it in the optimal method. Furthermore, it is also possible to analyze the user's past gourmet information usage history and select the most reliable gourmet information source. In this way, the gourmet information acquisition unit can analyze the user's past gourmet information usage history and select the optimal acquisition method.

[0061] The gourmet information acquisition unit can perform filtering based on the user's current areas of interest when acquiring gourmet information. The gourmet information acquisition unit performs filtering based on the user's current areas of interest when acquiring gourmet information. The current areas of interest can be identified, for example, from recent search keywords, browsing history, etc. The gourmet information acquisition unit filters unnecessary gourmet information based on the user's current areas of interest. For example, only information related to gourmet food in which the user is currently interested can be acquired. Unnecessary gourmet information can also be filtered based on the user's current areas of interest. Furthermore, the latest gourmet information related to the user's areas of interest can be preferentially acquired. This allows the gourmet information acquisition unit to filter gourmet information based on the user's current areas of interest and eliminate unnecessary information.

[0062] When acquiring gourmet information, the gourmet information acquisition unit can prioritize acquiring highly relevant gourmet information in consideration of the user's geographical location information. When acquiring gourmet information, the gourmet information acquisition unit prioritizes acquiring highly relevant gourmet information in consideration of the user's geographical location information. The geographical location information can be acquired from, for example, GPS data or an IP address. The gourmet information acquisition unit prioritizes acquiring nearby gourmet information based on the user's current location. For example, it prioritizes acquiring gourmet information related to the user's current location. It is also possible to prioritize acquiring nearby gourmet information based on the user's current location. Furthermore, it is also possible to prioritize acquiring nearby gourmet information based on the user's geographical location information. This allows the gourmet information acquisition unit to prioritize acquiring highly relevant gourmet information in consideration of the user's geographical location information.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The information acquisition unit can analyze the user's past travel history and predict the user's next travel destination. For example, the information acquisition unit can predict the user's next travel destination based on places the user has visited in the past, the length of stay, the purpose of the trip, etc. The information acquisition unit can also suggest travel destinations related to specific seasons or events based on the user's past travel history. Furthermore, the information acquisition unit can analyze the user's past travel history and suggest optimal travel plans. This allows the information acquisition unit to predict the user's next travel destination based on the user's past travel history and provide more appropriate information.

[0065] The display unit can adjust the display format based on the user's current activity status. For example, when the user is walking, a simple, highly visible display format can be adopted. When the user is driving a car, voice guidance can be given priority. Furthermore, when the user is taking a break, detailed information can be displayed. In this way, the display unit can adjust the display format according to the user's current activity status and provide more appropriate information.

[0066] The information acquisition unit can adjust the information acquisition method taking into account the user's current health condition. For example, if the user is in poor health, only the minimum necessary information is acquired. On the other hand, if the user is healthy, detailed information can be acquired frequently. Furthermore, the optimal information acquisition method can be selected depending on the user's health condition. This allows the information acquisition unit to adjust the information acquisition method depending on the user's health condition and provide more appropriate information.

[0067] The display unit can analyze the user's past display history and select the optimal display method. For example, it can prioritize the display format that the user has used favorably in the past. It can also select a display method suitable for a specific time period based on the user's past display history. It can also analyze the user's past display history and select the display method with the highest visibility. This allows the display unit to select the optimal display method based on the user's past display history and provide more appropriate information.

[0068] The information acquisition unit can select the optimal information acquisition method based on the user's current geographical location information. For example, if the user is in an urban area, real-time traffic information can be acquired preferentially. Also, if the user is in a tourist destination, information about tourist spots can be acquired preferentially. Furthermore, the most relevant information can be acquired based on the user's current location. This allows the information acquisition unit to select the information acquisition method according to the user's geographical location information and provide more appropriate information.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The information acquisition unit acquires information from sources on the Internet. The information acquisition unit can acquire information from news sites, social media, blogs, etc., and can acquire information using APIs or by scraping. Step 2: The analysis unit analyzes the information acquired by the information acquisition unit. The analysis unit can analyze the information using methods such as text analysis, image analysis, and data mining, and classify the acquired information and organize it based on importance and relevance. Step 3: The display unit displays the information analyzed by the analysis unit in a UI. The display unit displays the information in the form of a web page, a mobile app, a dashboard, etc., and can also visually display the information using graphs and charts so that the user can intuitively understand the information. Step 4: The linking unit links with the electronic payment system based on the information displayed by the display unit. The linking unit links with the electronic payment system by methods such as credit card payment, electronic money, and 2D code payment, and manages the system so that the user can smoothly make payments at stores near their destination.

[0071] (Example 2) The travel information integration system according to an embodiment of the present invention allows users to check information about their destinations on a single UI when traveling or going out. When a user sets a destination, the system retrieves, analyzes, and displays real-time information from online sources. The retrieved information includes weather, temperature, congestion, and restaurant information. This allows users to check the latest information about their destinations on a single system without having to navigate multiple websites or apps. The system can also be linked to an electronic payment system, allowing for smooth payments at stores near the destination. For example, when a user sets "Tokyo Station" as their destination, an information retrieval unit retrieves information about the weather, temperature, congestion, and restaurant information related to Tokyo Station from online sources. An analysis unit analyzes this information, and a display unit displays it on a single UI. Users can check the weather, temperature, congestion, and restaurant information at a glance. Furthermore, the linking unit links with an electronic payment system, allowing for smooth payments at stores near Tokyo Station. This system allows users to check the latest information about their destinations on a single system without having to navigate multiple websites or apps. Linking with an electronic payment system also allows for smooth payments at stores near the destination. This will enable the travel information integrated system to allow users to check information about their destination on a single UI, and it will also be possible to link with electronic payment systems.

[0072] The travel information integration system according to the embodiment includes an information acquisition unit, an analysis unit, a display unit, and a linking unit. The information acquisition unit acquires information from information sources on the Internet. The information acquisition unit can acquire information from, for example, news sites, social networking sites, blogs, etc. The information acquisition unit can acquire information using an API or by scraping. The analysis unit analyzes the information acquired by the information acquisition unit. The analysis unit analyzes the information using, for example, text analysis, image analysis, data mining, or other methods. The analysis unit can classify the acquired information and organize it based on importance or relevance. The display unit displays the information analyzed by the analysis unit in a UI. The display unit can display the information in the form of, for example, a webpage, a mobile app, a dashboard, or the like. The display unit can also visually display the information using graphs or charts to enable a user to intuitively understand the information. The linking unit links with an electronic payment system based on the information displayed by the display unit. The linking unit can link with the electronic payment system using, for example, credit card payment, electronic money, two-dimensional code (e.g., QR code) payment, or other methods. The linking unit manages linking with electronic payment systems so that users can smoothly make payments at stores near their destinations. As a result, the travel information integrated system according to the embodiment allows users to check information about their destinations on a single UI and also enables linking with electronic payment systems.

[0073] The travel information integrated system includes a weather information acquisition unit that acquires weather information. The weather information acquisition unit acquires weather information. The weather information acquisition unit can acquire weather information such as temperature, precipitation, and wind speed. The weather information acquisition unit can acquire weather information from data from the Japan Meteorological Agency or private weather services. The weather information acquisition unit can acquire weather information in real time and provide it to the user. For example, the weather information acquisition unit uses an API to acquire data from the Japan Meteorological Agency and update it in real time. The weather information acquisition unit can also acquire data provided by private weather services and provide it to the user. This allows the weather information acquisition unit to check the weather at the user's destination.

[0074] The travel information integrated system includes a temperature information acquisition unit that acquires temperature information. The temperature information acquisition unit acquires temperature information. The temperature information acquisition unit can acquire temperature information, for example, from data from the Japan Meteorological Agency or private weather services. The temperature information acquisition unit can acquire temperature information in real time and provide it to the user. For example, the temperature information acquisition unit acquires data from the Japan Meteorological Agency using an API and updates it in real time. The temperature information acquisition unit can also acquire data provided by private weather services and provide it to the user. This allows the temperature information acquisition unit to check the temperature at the user's destination.

[0075] The travel information integrated system includes a congestion status acquisition unit that acquires congestion status. The congestion status acquisition unit acquires congestion status. The congestion status acquisition unit can acquire congestion status from, for example, real-time people flow data or past statistical data. The congestion status acquisition unit can acquire congestion status in real time and provide it to the user. For example, the congestion status acquisition unit acquires real-time people flow data using an API and updates congestion status in real time. The congestion status acquisition unit can also predict congestion status based on past statistical data and provide it to the user. This allows the user to check the congestion status at their destination.

[0076] The travel information integrated system includes a gourmet information acquisition unit that acquires gourmet information. The gourmet information acquisition unit acquires gourmet information. The gourmet information acquisition unit can acquire gourmet information from, for example, restaurant review sites or posts on social media. The gourmet information acquisition unit can acquire gourmet information in real time and provide it to the user. For example, the gourmet information acquisition unit uses an API to acquire data from restaurant review sites and update it in real time. The gourmet information acquisition unit can also analyze posts on social media and provide it to the user. This allows the gourmet information acquisition unit to check gourmet information for the user's destination.

[0077] The linking unit links with an electronic payment system to enable payments at stores near the destination. The linking unit links with the electronic payment system. The linking unit can link with the electronic payment system by methods such as credit card payment, electronic money, and two-dimensional code payment. The linking unit manages the link with the electronic payment system so that the user can smoothly make payments at stores near the destination. For example, the linking unit allows the user to make payments through the electronic payment system by scanning a two-dimensional code. The linking unit can also perform authentication through the electronic payment system when the user makes a payment using a credit card. In this way, the linking unit enables the user to smoothly make payments at stores near the destination.

[0078] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. The information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The information acquisition unit adjusts the timing of information acquisition based on the estimated user's emotions. For example, if the user is feeling stressed, the information acquisition unit can reduce the frequency of information acquisition and acquire only the minimum amount of information necessary. Also, if the user is relaxed, detailed information can be acquired frequently and provided to the user. Furthermore, if the user is in a hurry, the most important information can be acquired preferentially and provided quickly. In this way, the information acquisition unit can adjust the timing of information acquisition according to the user's emotions and provide more appropriate information.

[0079] The information acquisition unit can analyze the user's past search history and select an appropriate information acquisition method. The information acquisition unit analyzes the user's past search history. The past search history can be acquired, for example, from browser history or search engine logs. The information acquisition unit selects the optimal information acquisition method based on the past search history. For example, it prioritizes the use of information sources that the user has frequently searched for in the past. It can also predict the type of information to be acquired during a specific time period from the user's past search history and acquire it in the optimal method. Furthermore, it can analyze the user's past search history and select the most reliable information source. This allows the information acquisition unit to analyze the user's past search history and select the optimal information acquisition method.

[0080] The information acquisition unit can perform filtering based on the user's current areas of interest when acquiring information. The information acquisition unit performs filtering based on the user's current areas of interest when acquiring information. The current areas of interest can be identified, for example, from recent search keywords, browsing history, etc. The information acquisition unit filters unnecessary information based on the user's current areas of interest. For example, only information related to topics in which the user is currently interested can be acquired. Unnecessary information can also be filtered based on the user's current areas of interest. Furthermore, the latest information related to the user's areas of interest can be preferentially acquired. This allows the information acquisition unit to filter information based on the user's current areas of interest and eliminate unnecessary information.

[0081] The information acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated emotions. The information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The information acquisition unit determines the priority of information to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize acquiring relaxing information. Also, if the user is excited, it can prioritize acquiring entertainment-related information. Furthermore, if the user is tired, it can prioritize acquiring refreshing information. In this way, the information acquisition unit can prioritize information according to the user's emotions and provide more appropriate information.

[0082] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. When acquiring information, the information acquisition unit prioritizes acquiring highly relevant information in consideration of the user's geographical location information. The geographical location information can be acquired from, for example, GPS data or an IP address. The information acquisition unit prioritizes acquiring nearby information based on the user's current location. For example, it prioritizes acquiring weather information related to the user's current location. It can also prioritize acquiring nearby congestion information based on the user's current location. Furthermore, it can also prioritize acquiring nearby gourmet information based on the user's geographical location information. This allows the information acquisition unit to prioritize acquiring highly relevant information in consideration of the user's geographical location information.

[0083] The information acquisition unit can analyze the user's social media activity when acquiring information and acquire related information. The information acquisition unit analyzes the user's social media activity when acquiring information. Social media activity can be acquired from, for example, the content of posts, the number of likes, the number of followers, etc. The information acquisition unit acquires related information based on the user's social media activity. For example, information related to places shared by the user on social media is preferentially acquired. Information related to topics of interest to the user can also be acquired from the user's social media activity. Furthermore, related information can be acquired based on the content of posts from accounts the user follows. This allows the information acquisition unit to analyze the user's social media activity and acquire related information.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. The analysis unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The analysis unit adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. Also, if the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, visually appealing analysis results can be provided. In this way, the analysis unit can adjust the way the analysis is presented according to the user's emotions and provide more appropriate analysis results.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. The importance of the information can be evaluated, for example, based on the user's level of interest or the reliability of the information. The analysis unit performs a detailed analysis of information with high importance. For example, detailed analysis results are provided for information with high importance. Also, a concise analysis can be performed for information with low importance. Furthermore, the analysis priority can be determined according to the importance of the information. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the information and perform efficient analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on the category of information during analysis. Information categories can be classified, for example, into news, entertainment, sports, etc. The analysis unit applies the most appropriate analysis algorithm depending on the category of information. For example, a weather data analysis algorithm can be applied to weather information. A real-time data analysis algorithm can also be applied to congestion status. Furthermore, a word-of-mouth data analysis algorithm can be applied to gourmet information. In this way, the analysis unit can apply different analysis algorithms depending on the category of information, allowing for more accurate analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Also, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. In this way, the analysis unit can adjust the length of the analysis according to the user's emotions and provide a more appropriate analysis result.

[0088] The analysis unit can determine the priority of analysis based on the time when the information was acquired during analysis. The analysis unit can determine the priority of analysis based on the time when the information was acquired during analysis. The time when the information was acquired can be evaluated, for example, from real-time data or past data. The analysis unit prioritizes analysis of the most recent information. For example, detailed analysis results can be provided for the most recent information. Also, a concise analysis can be performed for older information. Furthermore, the level of detail of the analysis can be adjusted depending on the time when the information was acquired. This allows the analysis unit to determine the priority of analysis based on the time when the information was acquired, and prioritize analysis of the most recent information.

[0089] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. The relevance of information can be evaluated, for example, based on the user's level of interest or the content of the information. The analysis unit prioritizes analysis of highly relevant information. For example, detailed analysis results are provided for highly relevant information. Also, a concise analysis can be performed for less relevant information. Furthermore, the analysis order can be determined according to the relevance of the information. In this way, the analysis unit can adjust the order of analysis based on the relevance of information and perform efficient analysis.

[0090] The display unit can estimate the user's emotion and adjust the display expression method based on the estimated emotion. The display unit estimates the user's emotion. The user's emotion can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The display unit adjusts the display expression method based on the estimated user's emotion. For example, if the user is relaxed, detailed information can be displayed. Also, if the user is in a hurry, concise information that focuses on the main points can be displayed. Furthermore, if the user is excited, visually appealing information can be displayed. In this way, the display unit can adjust the display expression method according to the user's emotion and provide more appropriate information.

[0091] The display unit can adjust the level of detail of the display based on the importance of the information when displaying the information. The display unit adjusts the level of detail of the display based on the importance of the information when displaying the information. The importance of the information can be evaluated, for example, based on the user's level of interest or the reliability of the information. The display unit displays information of high importance in detail. For example, detailed information is displayed for information of high importance. Also, information of low importance can be displayed in a concise manner. Furthermore, the display priority can be determined according to the importance of the information. In this way, the display unit can adjust the level of detail of the display based on the importance of the information and provide information efficiently.

[0092] The display unit can apply different display algorithms depending on the category of information when displaying the information. The display unit applies different display algorithms depending on the category of information when displaying the information. Information categories can be classified, for example, into news, entertainment, sports, etc. The display unit applies the most appropriate display algorithm depending on the category of information. For example, a weather data display algorithm can be applied to weather information. A real-time data display algorithm can also be applied to congestion status. Furthermore, a word-of-mouth data display algorithm can also be applied to gourmet information. In this way, the display unit can apply different display algorithms depending on the category of information and provide more accurate information.

[0093] The display unit can estimate the user's emotion and adjust the length of the display based on the estimated emotion. The display unit estimates the user's emotion. The user's emotion can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The display unit adjusts the length of the display based on the estimated user's emotion. For example, if the user is in a hurry, a short and to-the-point display can be provided. Alternatively, if the user is relaxed, a detailed display can be provided. Furthermore, if the user is excited, a visually appealing display can be provided. In this way, the display unit can adjust the length of the display according to the user's emotion and provide more appropriate information.

[0094] The display unit can determine the display priority based on the time when the information was acquired when it was displayed. The display unit can determine the display priority based on the time when the information was acquired when it was displayed. The time when the information was acquired can be evaluated, for example, from real-time data or past data. The display unit preferentially displays the latest information. For example, the latest information is displayed in detail. Old information can also be displayed in a concise manner. Furthermore, the level of detail of the display can be adjusted depending on the time when the information was acquired. This allows the display unit to determine the display priority based on the time when the information was acquired and preferentially display the latest information.

[0095] The display unit can adjust the display order based on the relevance of the information when displaying the information. The display unit adjusts the display order based on the relevance of the information when displaying the information. The relevance of the information can be evaluated, for example, based on the user's level of interest or the content of the information. The display unit preferentially displays highly relevant information. For example, highly relevant information is displayed in detail. Also, less relevant information can be displayed in a concise manner. Furthermore, the display order can be determined according to the relevance of the information. In this way, the display unit can adjust the display order based on the relevance of the information and provide information efficiently.

[0096] The collaboration unit can estimate the user's emotion and adjust the collaboration method based on the estimated emotion. The collaboration unit estimates the user's emotion. The user's emotion can be estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. The collaboration unit adjusts the collaboration method based on the estimated user's emotion. For example, if the user is relaxed, a detailed collaboration method can be provided. Also, if the user is in a hurry, a simple and quick collaboration method can be provided. Furthermore, if the user is excited, a visually appealing collaboration method can be provided. In this way, the collaboration unit can adjust the collaboration method according to the user's emotion and perform more appropriate collaboration.

[0097] At the time of collaboration, the collaboration unit can analyze the user's past collaboration history and select an appropriate collaboration method. At the time of collaboration, the collaboration unit analyzes the user's past collaboration history. The past collaboration history can be obtained, for example, from a collaboration log or the user's operation history. The collaboration unit selects the optimal collaboration method based on the past collaboration history. For example, the collaboration unit preferentially suggests collaboration methods that the user has used in the past. The collaboration unit can also select the most efficient collaboration method from the user's past collaboration history. Furthermore, the collaboration unit can analyze the user's past collaboration history and suggest the optimal collaboration method. This allows the collaboration unit to analyze the user's past collaboration history and select the optimal collaboration method.

[0098] The collaboration unit can customize the collaboration means based on the user's current living situation at the time of collaboration. The collaboration unit customizes the collaboration means based on the user's current living situation at the time of collaboration. The current living situation can be identified, for example, from the user's schedule or location information. The collaboration unit provides the optimal collaboration means according to the user's living situation. For example, if the user is busy, a quick collaboration means can be provided. Also, if the user is relaxed, a detailed collaboration means can be provided. Furthermore, the collaboration unit can suggest the optimal collaboration means according to the user's living situation. This allows the collaboration unit to customize the collaboration means according to the user's living situation and perform more appropriate collaboration.

[0099] The linking unit can estimate the user's emotion and determine the priority of linking based on the estimated emotion. The linking unit estimates the user's emotion. The user's emotion can be estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. The linking unit determines the priority of linking based on the estimated user's emotion. For example, if the user is relaxed, a detailed linking method can be provided preferentially. Also, if the user is in a hurry, a quick linking method can be provided preferentially. Furthermore, if the user is excited, a visually attractive linking method can be provided preferentially. In this way, the linking unit can determine the priority of linking according to the user's emotion and perform more appropriate linking.

[0100] The collaboration unit can select an appropriate collaboration method in consideration of the user's geographical location information when collaborating. The collaboration unit selects the optimal collaboration method in consideration of the user's geographical location information when collaborating. Geographical location information can be acquired from, for example, GPS data or an IP address. The collaboration unit provides the optimal collaboration method based on the user's current location. For example, the collaboration unit can suggest the optimal collaboration method based on the user's current location. It can also provide a collaboration method with nearby stores based on the user's geographical location information. Furthermore, it can select the most efficient collaboration method according to the user's current location. This allows the collaboration unit to select the optimal collaboration method in consideration of the user's geographical location information.

[0101] The collaboration unit can analyze the user's social media activity at the time of collaboration and suggest collaboration methods. The collaboration unit analyzes the user's social media activity at the time of collaboration. Social media activity can be obtained, for example, from the content of posts, the number of likes, the number of followers, etc. The collaboration unit suggests the optimal collaboration method based on the user's social media activity. For example, the collaboration unit can suggest collaboration methods related to places the user has shared on social media. The collaboration unit can also provide collaboration methods with stores of interest based on the user's social media activity. Furthermore, the collaboration unit can suggest the optimal collaboration method based on the content of posts from accounts the user follows. This allows the collaboration unit to analyze the user's social media activity and suggest the optimal collaboration method.

[0102] The weather information acquisition unit can estimate the user's emotions and adjust the timing of weather information acquisition based on the estimated emotions. The weather information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The weather information acquisition unit adjusts the timing of weather information acquisition based on the estimated user's emotions. For example, if the user is feeling stressed, the weather information acquisition unit can reduce the frequency of weather information acquisition and acquire only the minimum amount of information necessary. Also, if the user is relaxed, detailed weather information can be acquired frequently and provided to the user. Furthermore, if the user is in a hurry, the most important weather information can be acquired first and provided quickly. In this way, the weather information acquisition unit can adjust the timing of weather information acquisition according to the user's emotions and provide more appropriate weather information.

[0103] When acquiring weather information, the weather information acquisition unit can analyze the user's past weather information usage history and select an appropriate acquisition method. When acquiring weather information, the weather information acquisition unit analyzes the user's past weather information usage history. The past weather information usage history can be acquired, for example, from a weather app usage history or search history. The weather information acquisition unit selects the optimal acquisition method based on the past weather information usage history. For example, it prioritizes the use of weather information sources that the user has frequently used in the past. It can also predict the type of weather information to be acquired during a specific time period from the user's past weather information usage history and acquire it using the optimal method. It can also analyze the user's past weather information usage history and select the most reliable weather information source. This allows the weather information acquisition unit to analyze the user's past weather information usage history and select the optimal acquisition method.

[0104] The weather information acquisition unit can filter the weather information based on the user's current areas of interest when acquiring the weather information. The weather information acquisition unit can filter the weather information based on the user's current areas of interest when acquiring the weather information. The current areas of interest can be identified, for example, from recent search keywords or browsing history. The weather information acquisition unit filters unnecessary weather information based on the user's current areas of interest. For example, only information related to weather in which the user is currently interested can be acquired. Unnecessary weather information can also be filtered based on the user's current areas of interest. Furthermore, the latest weather information related to the user's areas of interest can be preferentially acquired. This allows the weather information acquisition unit to filter weather information based on the user's current areas of interest and eliminate unnecessary information.

[0105] The weather information acquisition unit can estimate the user's emotions and determine the priority of weather information to be acquired based on the estimated emotions. The weather information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The weather information acquisition unit determines the priority of weather information to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize acquiring relaxing weather information. Also, if the user is excited, it can prioritize acquiring entertainment-related weather information. Furthermore, if the user is tired, it can prioritize acquiring refreshing weather information. In this way, the weather information acquisition unit can prioritize weather information according to the user's emotions and provide more appropriate weather information.

[0106] When acquiring weather information, the weather information acquisition unit can prioritize acquiring highly relevant weather information taking into account the user's geographical location information. When acquiring weather information, the weather information acquisition unit prioritizes acquiring highly relevant weather information taking into account the user's geographical location information. Geographical location information can be acquired from, for example, GPS data or an IP address. The weather information acquisition unit prioritizes acquiring nearby weather information based on the user's current location. For example, it prioritizes acquiring weather information related to the user's current location. It can also prioritize acquiring nearby weather information based on the user's current location. Furthermore, it can also prioritize acquiring nearby weather information based on the user's geographical location information. This allows the weather information acquisition unit to prioritize acquiring highly relevant weather information taking into account the user's geographical location information.

[0107] The temperature information acquisition unit can estimate the user's emotions and adjust the timing of acquiring temperature information based on the estimated emotions. The temperature information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The temperature information acquisition unit adjusts the timing of acquiring temperature information based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of acquiring temperature information can be reduced and only the minimum amount of information necessary can be acquired. Also, if the user is relaxed, detailed temperature information can be acquired frequently and provided to the user. Furthermore, if the user is in a hurry, the most important temperature information can be acquired preferentially and provided quickly. In this way, the temperature information acquisition unit can adjust the timing of acquiring temperature information according to the user's emotions and provide more appropriate temperature information.

[0108] When acquiring temperature information, the temperature information acquisition unit can analyze the user's past temperature information usage history and select an appropriate acquisition method. When acquiring temperature information, the temperature information acquisition unit analyzes the user's past temperature information usage history. The past temperature information usage history can be acquired, for example, from the temperature app usage history or search history. The temperature information acquisition unit selects the optimal acquisition method based on the past temperature information usage history. For example, it prioritizes the use of temperature information sources that the user has frequently used in the past. It can also predict the type of temperature information to be acquired during a specific time period from the user's past temperature information usage history and acquire it using the optimal method. It can also analyze the user's past temperature information usage history and select the most reliable temperature information source. In this way, the temperature information acquisition unit can analyze the user's past temperature information usage history and select the optimal acquisition method.

[0109] The temperature information acquisition unit can perform filtering based on the user's current areas of interest when acquiring temperature information. The temperature information acquisition unit performs filtering based on the user's current areas of interest when acquiring temperature information. The current areas of interest can be identified, for example, from recent search keywords or browsing history. The temperature information acquisition unit filters unnecessary temperature information based on the user's current areas of interest. For example, only information related to temperatures in which the user is currently interested can be acquired. Unnecessary temperature information can also be filtered based on the user's current areas of interest. Furthermore, the latest temperature information related to the user's areas of interest can be preferentially acquired. This allows the temperature information acquisition unit to filter temperature information based on the user's current areas of interest and eliminate unnecessary information.

[0110] The temperature information acquisition unit can estimate the user's emotions and determine the priority of temperature information to be acquired based on the estimated emotions. The temperature information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The temperature information acquisition unit determines the priority of temperature information to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize acquiring temperature information that will help the user relax. Also, if the user is excited, it can prioritize acquiring entertainment-related temperature information. Furthermore, if the user is tired, it can prioritize acquiring refreshing temperature information. In this way, the temperature information acquisition unit can prioritize temperature information according to the user's emotions and provide more appropriate temperature information.

[0111] When acquiring temperature information, the temperature information acquisition unit can prioritize acquiring highly relevant temperature information in consideration of the user's geographical location information. When acquiring temperature information, the temperature information acquisition unit prioritizes acquiring highly relevant temperature information in consideration of the user's geographical location information. Geographical location information can be acquired from, for example, GPS data or an IP address. The temperature information acquisition unit prioritizes acquiring nearby temperature information based on the user's current location. For example, it prioritizes acquiring temperature information related to the user's current location. It can also prioritize acquiring nearby temperature information based on the user's current location. Furthermore, it can also prioritize acquiring nearby temperature information based on the user's geographical location information. This allows the temperature information acquisition unit to prioritize acquiring highly relevant temperature information in consideration of the user's geographical location information.

[0112] The congestion status acquisition unit can estimate the user's emotions and adjust the timing of acquiring the congestion status based on the estimated emotions. The congestion status acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The congestion status acquisition unit adjusts the timing of acquiring the congestion status based on the estimated user's emotions. For example, if the user is feeling stressed, the congestion status acquisition unit reduces the frequency of acquiring the congestion status and acquires only the minimum necessary information. Also, if the user is relaxed, detailed congestion status can be acquired frequently and provided to the user. Furthermore, if the user is in a hurry, the most important congestion status can be acquired preferentially and provided quickly. In this way, the congestion status acquisition unit can adjust the timing of acquiring the congestion status according to the user's emotions and provide more appropriate congestion status information.

[0113] When acquiring congestion status, the congestion status acquisition unit can analyze the user's past congestion status usage history and select an appropriate acquisition method. When acquiring congestion status, the congestion status acquisition unit analyzes the user's past congestion status usage history. The past congestion status usage history can be acquired, for example, from the usage history or search history of a congestion status app. The congestion status acquisition unit selects the optimal acquisition method based on the past congestion status usage history. For example, the congestion status acquisition unit prioritizes the use of congestion status information sources that the user has frequently used in the past. In addition, the type of congestion status to be acquired during a specific time period can be predicted from the user's past congestion status usage history and acquired using the optimal method. Furthermore, the user's past congestion status usage history can be analyzed to select the most reliable congestion status information source. In this way, the congestion status acquisition unit can analyze the user's past congestion status usage history and select the optimal acquisition method.

[0114] The congestion status acquisition unit can perform filtering based on the user's current area of ​​interest when acquiring the congestion status. The congestion status acquisition unit performs filtering based on the user's current area of ​​interest when acquiring the congestion status. The current area of ​​interest can be identified, for example, from recent search keywords or browsing history. The congestion status acquisition unit filters unnecessary congestion status information based on the user's current area of ​​interest. For example, only information related to congestion status that the user is currently interested in can be acquired. In addition, unnecessary congestion status information can be filtered based on the user's current area of ​​interest. Furthermore, the latest congestion status information related to the user's area of ​​interest can be preferentially acquired. In this way, the congestion status acquisition unit can filter congestion status information based on the user's current area of ​​interest and eliminate unnecessary information.

[0115] The congestion status acquisition unit can estimate the user's emotions and determine the priority of congestion status to be acquired based on the estimated emotions. The congestion status acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The congestion status acquisition unit determines the priority of congestion status to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, congestion status information that will help the user relax can be preferentially acquired. Also, if the user is excited, congestion status information related to entertainment can be preferentially acquired. Furthermore, if the user is tired, congestion status information that will help the user refresh can be preferentially acquired. In this way, the congestion status acquisition unit can determine the priority of congestion status according to the user's emotions and provide more appropriate congestion status information.

[0116] When acquiring congestion status, the congestion status acquisition unit can prioritize acquiring highly relevant congestion status in consideration of the user's geographical location information. When acquiring congestion status, the congestion status acquisition unit prioritizes acquiring highly relevant congestion status in consideration of the user's geographical location information. The geographical location information can be acquired from, for example, GPS data or an IP address. The congestion status acquisition unit prioritizes acquiring nearby congestion status based on the user's current location. For example, congestion status information related to the user's current location can be prioritized. Also, nearby congestion status information can be prioritized based on the user's current location. Furthermore, nearby congestion status information can be prioritized based on the user's geographical location information. This allows the congestion status acquisition unit to prioritize acquiring highly relevant congestion status in consideration of the user's geographical location information.

[0117] The gourmet information acquisition unit can estimate the user's emotions and adjust the timing of acquiring gourmet information based on the estimated emotions. The gourmet information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The gourmet information acquisition unit adjusts the timing of acquiring gourmet information based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of acquiring gourmet information can be reduced and only the minimum amount of information necessary can be acquired. Also, if the user is relaxed, detailed gourmet information can be acquired frequently and provided to the user. Furthermore, if the user is in a hurry, the most important gourmet information can be acquired preferentially and provided quickly. In this way, the gourmet information acquisition unit can adjust the timing of acquiring gourmet information according to the user's emotions and provide more appropriate gourmet information.

[0118] When acquiring gourmet information, the gourmet information acquisition unit can analyze the user's past gourmet information usage history and select an appropriate acquisition method. When acquiring gourmet information, the gourmet information acquisition unit analyzes the user's past gourmet information usage history. The past gourmet information usage history can be acquired, for example, from a gourmet app usage history or a search history. The gourmet information acquisition unit selects the optimal acquisition method based on the past gourmet information usage history. For example, it prioritizes the use of gourmet information sources that the user has frequently used in the past. It is also possible to predict the type of gourmet information to be acquired during a specific time period from the user's past gourmet information usage history and acquire it in the optimal method. Furthermore, it is also possible to analyze the user's past gourmet information usage history and select the most reliable gourmet information source. In this way, the gourmet information acquisition unit can analyze the user's past gourmet information usage history and select the optimal acquisition method.

[0119] The gourmet information acquisition unit can perform filtering based on the user's current areas of interest when acquiring gourmet information. The gourmet information acquisition unit performs filtering based on the user's current areas of interest when acquiring gourmet information. The current areas of interest can be identified, for example, from recent search keywords, browsing history, etc. The gourmet information acquisition unit filters unnecessary gourmet information based on the user's current areas of interest. For example, only information related to gourmet food in which the user is currently interested can be acquired. Unnecessary gourmet information can also be filtered based on the user's current areas of interest. Furthermore, the latest gourmet information related to the user's areas of interest can be preferentially acquired. This allows the gourmet information acquisition unit to filter gourmet information based on the user's current areas of interest and eliminate unnecessary information.

[0120] The gourmet information acquisition unit can estimate the user's emotions and determine the priority of gourmet information to be acquired based on the estimated emotions. The gourmet information acquisition unit estimates the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. The gourmet information acquisition unit determines the priority of gourmet information to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, gourmet information that will help them relax can be acquired preferentially. Also, if the user is excited, entertainment-related gourmet information can be acquired preferentially. Furthermore, if the user is tired, gourmet information that will help them refresh can be acquired preferentially. In this way, the gourmet information acquisition unit can determine the priority of gourmet information according to the user's emotions and provide more appropriate gourmet information.

[0121] When acquiring gourmet information, the gourmet information acquisition unit can prioritize acquiring highly relevant gourmet information in consideration of the user's geographical location information. When acquiring gourmet information, the gourmet information acquisition unit prioritizes acquiring highly relevant gourmet information in consideration of the user's geographical location information. The geographical location information can be acquired from, for example, GPS data or an IP address. The gourmet information acquisition unit prioritizes acquiring nearby gourmet information based on the user's current location. For example, it prioritizes acquiring gourmet information related to the user's current location. It is also possible to prioritize acquiring nearby gourmet information based on the user's current location. Furthermore, it is also possible to prioritize acquiring nearby gourmet information based on the user's geographical location information. This allows the gourmet information acquisition unit to prioritize acquiring highly relevant gourmet information in consideration of the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the information acquisition unit, analysis unit, display unit, and linking unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information acquisition unit can acquire information from an information source on the Internet via the communication I / F 44 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The display unit displays the analyzed information using the display 40A of the smart device 14. The linking unit can link with an electronic payment system via the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the information acquisition unit, analysis unit, display unit, and linking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information acquisition unit can acquire information from an information source on the Internet via the communication I / F 44 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The display unit displays the analyzed information using the display of the smart glasses 214. The linking unit can link with an electronic payment system via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the information acquisition unit, analysis unit, display unit, and linking unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the information acquisition unit can acquire information from an information source on the Internet via the communication I / F 44 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The display unit displays the analyzed information using the display 343 of the headset type terminal 314. The linking unit can link with an electronic payment system via the communication I / F 44 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the information acquisition unit, analysis unit, display unit, and linking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information acquisition unit can acquire information from an information source on the Internet via the communication I / F 44 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The display unit displays the analyzed information using the display of the robot 414. The linking unit can link with an electronic payment system via the communication I / F 44 of the robot 414.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The information acquisition unit can analyze the user's past travel history and predict the user's next travel destination. For example, the information acquisition unit can predict the user's next travel destination based on places the user has visited in the past, the length of stay, the purpose of the trip, etc. The information acquisition unit can also suggest travel destinations related to specific seasons or events based on the user's past travel history. Furthermore, the information acquisition unit can analyze the user's past travel history and suggest optimal travel plans. This allows the information acquisition unit to predict the user's next travel destination based on the user's past travel history and provide more appropriate information.

[0124] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis accuracy can be increased to provide more accurate information. Also, if the user is relaxed, the analysis accuracy can be adjusted to provide the minimum necessary information. Furthermore, if the user is in a hurry, the analysis accuracy can be adjusted to provide information quickly. In this way, the analysis unit can adjust the analysis accuracy according to the user's emotions and provide more appropriate information.

[0125] The display unit can adjust the display format based on the user's current activity status. For example, when the user is walking, a simple, highly visible display format can be adopted. When the user is driving a car, voice guidance can be given priority. Furthermore, when the user is taking a break, detailed information can be displayed. In this way, the display unit can adjust the display format according to the user's current activity status and provide more appropriate information.

[0126] The linking unit can estimate the user's emotions and select services to link based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize linking to services that help the user relax. Also, if the user is excited, it can prioritize linking to entertainment-related services. Furthermore, if the user is tired, it can prioritize linking to services that help the user refresh. In this way, the linking unit can select services to link based on the user's emotions and provide more appropriate services.

[0127] The information acquisition unit can adjust the information acquisition method taking into account the user's current health condition. For example, if the user is in poor health, only the minimum necessary information is acquired. On the other hand, if the user is healthy, detailed information can be acquired frequently. Furthermore, the optimal information acquisition method can be selected depending on the user's health condition. This allows the information acquisition unit to adjust the information acquisition method depending on the user's health condition and provide more appropriate information.

[0128] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize analyzing information that helps them relax. Also, if the user is excited, it can prioritize analyzing entertainment-related information. Furthermore, if the user is tired, it can prioritize analyzing information that helps them refresh. In this way, the analysis unit can determine the priority of analysis according to the user's emotions and provide more appropriate information.

[0129] The display unit can analyze the user's past display history and select the optimal display method. For example, it can prioritize the display format that the user has used favorably in the past. It can also select a display method suitable for a specific time period based on the user's past display history. It can also analyze the user's past display history and select the display method with the highest visibility. This allows the display unit to select the optimal display method based on the user's past display history and provide more appropriate information.

[0130] The linking unit can estimate the user's emotions and adjust the timing of linking based on the estimated emotions. For example, if the user is feeling stressed, the frequency of linking can be reduced and only the minimum necessary linking can be performed. Also, if the user is relaxed, detailed linking can be performed more frequently. Furthermore, if the user is in a hurry, the most important linking can be performed with priority. In this way, the linking unit can adjust the timing of linking according to the user's emotions and perform more appropriate linking.

[0131] The information acquisition unit can select the optimal information acquisition method based on the user's current geographical location information. For example, if the user is in an urban area, real-time traffic information can be acquired preferentially. Also, if the user is in a tourist destination, information about tourist spots can be acquired preferentially. Furthermore, the most relevant information can be acquired based on the user's current location. This allows the information acquisition unit to select the information acquisition method according to the user's geographical location information and provide more appropriate information.

[0132] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling stressed, the analysis method can be simplified to provide information quickly. Alternatively, if the user is relaxed, a detailed analysis can be performed to provide more information. Furthermore, if the user is in a hurry, the most important information can be prioritized in the analysis. This allows the analysis unit to adjust the analysis method according to the user's emotions and provide more appropriate information.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The information acquisition unit acquires information from sources on the Internet. The information acquisition unit can acquire information from news sites, social media, blogs, etc., and can acquire information using APIs or by scraping. Step 2: The analysis unit analyzes the information acquired by the information acquisition unit. The analysis unit can analyze the information using methods such as text analysis, image analysis, and data mining, and classify the acquired information and organize it based on importance and relevance. Step 3: The display unit displays the information analyzed by the analysis unit in a UI. The display unit displays the information in the form of a web page, a mobile app, a dashboard, etc., and can also visually display the information using graphs and charts so that the user can intuitively understand the information. Step 4: The linking unit links with the electronic payment system based on the information displayed by the display unit. The linking unit links with the electronic payment system by methods such as credit card payment, electronic money, and 2D code payment, and manages the system so that the user can smoothly make payments at stores near their destination.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0183] 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.

[0184] 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.

[0185] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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. an information acquisition unit that acquires information from sources on the Internet; an analysis unit that analyzes the information acquired by the information acquisition unit; a display unit that displays the information analyzed by the analysis unit on a UI; a linking unit that links with an electronic payment system based on the information displayed by the display unit; Equipped with A system characterized by:

2. Equipped with a weather information acquisition unit that acquires weather information The system of claim 1 .

3. Equipped with a temperature information acquisition unit that acquires temperature information The system of claim 1 .

4. Equipped with a congestion status acquisition unit that acquires congestion status The system of claim 1 .

5. A gourmet information acquisition unit is provided to acquire gourmet information. The system of claim 1 .

6. The linking unit is Linked to electronic payment systems, it allows you to make payments at stores near your destination. The system of claim 1 .

7. The information acquisition unit Estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. The system of claim 1 .

8. The information acquisition unit Analyze users' past search history and select the appropriate information acquisition method The system of claim 1 .

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