system

Abstract

The system according to the embodiment aims to enable even users who are not familiar with using smartphones to easily gather information and provide route guidance. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a guidance unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit makes proposals based on the information analyzed by the analysis unit. The guidance unit provides route guidance based on the information proposed by the proposal 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, users had to use multiple apps to gather information and get route guidance, which was stressful for users who were not accustomed to using smartphones.

[0005] The system according to the embodiment aims to enable even users who are not familiar with using smartphones to easily gather information and provide route guidance. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a guidance unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit makes proposals based on the information analyzed by the analysis unit. The guidance unit provides route guidance based on the information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even users who are not familiar with using smartphones to easily gather information and provide route guidance. [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 super AI app according to an embodiment of the present invention is a system that gently supports people who are not familiar with smartphones or who are tired of switching between multiple apps to gather information. For example, when craving a delicious lunch, conventionally, users would have to use multiple apps to gather information, decide on a restaurant, search for a route, and check the weather, which requires a lot of effort. However, with this super AI app, users can simply launch a single app and, through simple conversation, describe what they want to eat, and the app will recommend restaurant menu items. For example, by asking "What's it like?", reviews will appear. By asking "How about if we go now?", a map showing the route and train information will appear. All users need to do is follow the instructions. If they don't like the menu item, they can simply say, "I want something different in a similar category," and the app will make recommendations accordingly. This eliminates the need to use multiple apps and allows for a single app to handle everything from searching for what they want to eat, selecting a restaurant, providing route guidance, and providing review information, all in one place, based on simple user conversations. Even those unfamiliar with smartphones can easily use the super AI app.

[0029] The super AI app according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, text information, voice information, and image information. The reception unit can accept the user information using, for example, voice input. The reception unit can also accept the user information using text input. The reception unit can also accept the user information using image input. For example, the reception unit converts the user's voice information into text information using voice recognition technology and transmits the text information to the analysis unit. The analysis unit analyzes the information accepted by the reception unit. The analysis unit analyzes the user information using, for example, a generation AI. The generation AI is realized using, for example, technology such as GPT-4 (registered trademark) or Gemini. The analysis unit analyzes the user information and transmits the results to the suggestion unit. The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestion unit makes optimal suggestions to the user using, for example, the generation AI. The suggestion unit, for example, presents recommendations to the user on a restaurant menu basis. The suggestion unit can also display review information to the user, for example, using a generation AI. The guidance unit provides route guidance based on the information suggested by the suggestion unit. The guidance unit, for example, uses a generation AI to provide a map showing the route and information on the train to take. The guidance unit, for example, guides the user to the optimal route from their current location to their destination. The guidance unit can also guide the user to the means of transportation from their current location to their destination, for example. This allows the super AI app according to the embodiment to efficiently accept, analyze, suggest, and guide the user's information.

[0030] The suggestion unit can use the generation AI to present recommendations by restaurant menu item. The suggestion unit uses the generation AI, for example, to suggest restaurant menu items that are optimal for the user. The generation AI is realized using technologies such as GPT-4 and Gemini. The suggestion unit presents recommendations based on the user's preferences and past usage history, for example. For example, if the user says, "I want to eat delicious ramen," the generation AI analyzes that information and searches for the optimal restaurant. Next, the suggestion unit presents recommendations by restaurant menu item based on the search results. For example, "I recommend the special ramen at this ramen restaurant." In this way, the generation AI can be used to suggest restaurant menu items that are optimal for the user.

[0031] The suggestion unit can display review information using a generation AI. The suggestion unit provides review information to users using, for example, a generation AI. The generation AI is realized using technologies such as GPT-4 and Gemini. For example, when a user asks, "What's it like?", the suggestion unit displays review information using the generation AI. For example, information such as "This ramen shop has high word-of-mouth reviews, and is particularly known for its delicious soup" is displayed. In this way, review information can be provided to users using the generation AI.

[0032] The guidance unit can provide a map showing the route or information about the train to take using the generation AI. The guidance unit provides optimal route guidance to the user using, for example, the generation AI. The generation AI is realized using technologies such as GPT-4 and Gemini. For example, when the user asks, "How about going now?", the generation AI provides a map showing the route and information about the train to take. For example, it might say, "It's a 10-minute walk to the nearest station, then get off the train two stops away, and then it's a 5-minute walk from there." In this way, the generation AI can provide optimal route guidance to the user.

[0033] The suggestion unit can use the generation AI to search again and present different recommendations. The suggestion unit provides different recommendations to the user, for example, using the generation AI. The generation AI is realized using technologies such as GPT-4 and Gemini. For example, if the user says, "I want something different in a similar category," the generation AI will search again and present a different recommendation. For example, the suggestion unit could say, "If you don't like the special ramen at this ramen shop, I recommend the soy sauce ramen at another ramen shop nearby." In this way, the generation AI can provide different recommendations to the user.

[0034] The reception unit can analyze the user's past usage history and select a reception method. The reception unit, for example, analyzes the user's past usage history and selects the optimal reception method. The past usage history includes, for example, past search history, purchase history, browsing history, etc. The reception unit, for example, automatically displays information that the user has frequently used in the past as a candidate. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past usage history. This makes it possible to provide the optimal reception method based on the user's past usage history.

[0035] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving information. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving information. The current situation includes, for example, a current location, a time zone, and weather. The areas of interest include, for example, past search keywords and browsing history. For example, when a user inputs their current situation, the reception unit preferentially displays related information. The reception unit can also filter and display highly relevant information based on the user's areas of interest. Furthermore, the reception unit can exclude unnecessary information and display only necessary information according to the user's current situation. This makes it possible to provide highly relevant information based on the user's current situation and areas of interest.

[0036] The reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving highly relevant information in consideration of the user's geographical location information when receiving information. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user inputs their current location, the reception unit prioritizes displaying nearby information. The reception unit can also filter and display highly relevant information based on the user's geographical location information. Furthermore, the reception unit can exclude unnecessary information and display only necessary information according to the user's current location. This makes it possible to provide highly relevant information based on the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity when receiving information and receive related information. For example, the reception unit analyzes the user's social media activity when receiving information and receives related information. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit can analyze the user's social media activity and preferentially display related information, for example. The reception unit can also filter and display highly relevant information based on the user's areas of interest in social media. Furthermore, the reception unit can exclude unnecessary information and display only necessary information according to the user's social media activity. This makes it possible to provide highly relevant information based on the user's social media activity.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, 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 is evaluated based on criteria such as the user's level of interest and the urgency of the information. For example, the analysis unit performs a detailed analysis of important information to provide a deeper understanding. The analysis unit can also perform a concise analysis of general information to quickly provide results. Furthermore, the analysis unit can omit analysis of unnecessary information and provide only the necessary information. This makes it possible to provide analysis results with an appropriate level of detail based on the importance of the information.

[0039] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. Information categories include, for example, a news category, an entertainment category, and a technology category. Analysis algorithms include, for example, a clustering algorithm and a classification algorithm. For example, the analysis unit applies an analysis algorithm that emphasizes word-of-mouth reviews to restaurant information. The analysis unit can also apply an analysis algorithm that emphasizes real-time traffic conditions to traffic information. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes the latest weather forecast to weather information. This makes it possible to apply the optimal analysis algorithm depending on the category of information.

[0040] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of information during analysis. The time of submission of information includes, for example, the submission date and time, the submission order, etc. The analysis unit, for example, prioritizes analysis of the latest information and provides results quickly. The analysis unit can also lower the analysis priority of old information and perform analysis as needed. Furthermore, the analysis unit can also determine the priority of information whose submission time is unknown by comparing it with other information. This allows analysis to be performed with appropriate priority based on the time of submission of the information.

[0041] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. The relevance of information is evaluated based on criteria such as common keywords and related topics. For example, the analysis unit prioritizes analysis of highly relevant information and provides results quickly. The analysis unit can also postpone the analysis of less relevant information and perform analysis as needed. Furthermore, the analysis unit can determine the order of analysis for information whose relevance is unknown by comparing it with other information. This allows analysis to be performed in an appropriate order based on the relevance of the information.

[0042] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the information when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the information when making a suggestion. The importance of the information is evaluated based on criteria such as the user's interest and the urgency of the information. For example, the suggestion unit makes detailed suggestions for important information to provide a deeper understanding. The suggestion unit can also make concise suggestions for general information to provide quick results. Furthermore, the suggestion unit can omit suggestions for unnecessary information and provide only the necessary information. This makes it possible to provide suggestions with an appropriate level of detail based on the importance of the information.

[0043] The suggestion unit can apply different suggestion algorithms depending on the category of information when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of information when making a suggestion. Information categories include, for example, a news category, an entertainment category, and a technology category. Proposal algorithms include, for example, a recommendation algorithm and a personalized algorithm. For example, the suggestion unit applies a suggestion algorithm that emphasizes word-of-mouth reviews to restaurant information. Furthermore, the suggestion unit can also apply a suggestion algorithm that emphasizes real-time traffic conditions to traffic information. Furthermore, the suggestion unit can apply a suggestion algorithm that emphasizes the latest weather forecast to weather information. This makes it possible to apply the optimal suggestion algorithm depending on the category of information.

[0044] The suggestion unit can determine the priority of the proposal based on the time of submission of the information at the time of proposal. For example, the suggestion unit determines the priority of the proposal based on the time of submission of the information at the time of proposal. The time of submission of the information includes, for example, the submission date and time, the submission order, etc. The suggestion unit, for example, gives priority to the latest information in making a proposal and provides results quickly. The suggestion unit can also lower the priority of the proposal for older information and make a proposal as needed. Furthermore, the suggestion unit can also determine the priority of information whose submission time is unknown by comparing it with other information. This allows suggestions to be made with appropriate priority based on the time of submission of the information.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of information when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of information when making suggestions. The relevance of information is evaluated based on criteria such as common keywords and related topics. For example, the suggestion unit prioritizes suggestions for highly relevant information and provides results quickly. The suggestion unit can also postpone the order of suggestions for less relevant information and make suggestions as needed. Furthermore, the suggestion unit can determine the order of suggestions for information whose relevance is unknown by comparing it with other information. This makes it possible to make suggestions in an appropriate order based on the relevance of information.

[0046] The guidance unit can analyze the user's past behavioral history and select the optimal guidance method when providing guidance. For example, the guidance unit analyzes the user's past behavioral history and selects the optimal guidance method when providing guidance. The past behavioral history includes, for example, past search history, purchase history, browsing history, etc. The guidance unit proposes the optimal guidance method, for example, based on routes the user has used in the past. The guidance unit can also propose a guidance method that avoids congestion based on the user's past behavioral history. Furthermore, the guidance unit can analyze the user's past behavioral history and propose the most efficient guidance method. This makes it possible to provide the optimal guidance method based on the user's past behavioral history.

[0047] The guidance unit can customize the guidance means based on the user's current situation when providing guidance. For example, the guidance unit customizes the guidance means based on the user's current situation when providing guidance. The current situation includes, for example, the current location, the time of day, the weather, etc. For example, if the user is in a hurry, the guidance unit can guide the user to the shortest route. Furthermore, if the user is relaxed, the guidance unit can also guide the user to a scenic route. Furthermore, if the user is tired, the guidance unit can guide the user to a route that includes rest points. This makes it possible to provide the optimal guidance means according to the user's current situation.

[0048] The guidance unit can select the optimal guidance method by taking into consideration the user's geographical location information when providing guidance. For example, the guidance unit selects the optimal guidance method by taking into consideration the user's geographical location information when providing guidance. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user inputs their current location, the guidance unit prioritizes displaying nearby information. The guidance unit can also filter and display highly relevant information based on the user's geographical location information. Furthermore, the guidance unit can exclude unnecessary information and display only necessary information according to the user's current location. This makes it possible to provide the optimal guidance method based on the user's geographical location information.

[0049] The guidance unit can analyze the user's social media activity and suggest guidance means when providing guidance. For example, the guidance unit analyzes the user's social media activity and suggests guidance means when providing guidance. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. For example, the guidance unit analyzes the user's social media activity and prioritizes displaying relevant information. The guidance unit can also filter and display highly relevant information based on the user's areas of interest in social media. Furthermore, the guidance unit can exclude unnecessary information and display only necessary information according to the user's social media activity. This makes it possible to provide optimal guidance means based on the user's social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can analyze a user's past search history and behavioral patterns to predict the information the user will need next. For example, if a user has frequently searched for specific information during a specific time period in the past, the analysis unit will prioritize analyzing information related to that time period. Also, if a user frequently searches for information in a specific category, the analysis unit can prioritize analyzing information related to that category. Furthermore, the analysis unit can predict the information the user will need next based on the user's behavioral patterns and perform analysis in advance. This allows the information the user needs to be provided quickly.

[0052] The guidance unit can adjust the guidance method taking into account the user's current health condition. For example, if the user is tired, the guidance unit can guide the user along a route that includes rest points. Also, if the user is in good health, the guidance unit can suggest active means of transportation such as walking or cycling. Furthermore, if the user has a specific health problem, the guidance unit can provide a guidance method that takes that problem into consideration. This enables optimal guidance according to the user's health condition.

[0053] The reception unit monitors the user's device usage status and can receive information at the optimal timing. For example, if the user is using another app, the reception unit waits until the user finishes using that app. Also, if the user is using the device for a long time, the reception unit can display a message encouraging the user to take a break. Furthermore, if the user is not using the device, the reception unit can send a notification to encourage the user to receive information. This makes it possible to receive information at an appropriate timing according to the user's device usage status.

[0054] The suggestion unit can analyze the user's social media activity and make suggestions based on the user's interests. For example, if the user frequently posts about a particular topic, the suggestion unit can prioritize suggesting information related to that topic. Also, if the user follows a particular account, the suggestion unit can suggest information related to that account. Furthermore, if the user uses a particular hashtag, the suggestion unit can suggest information related to that hashtag. This enables appropriate suggestions based on the user's social media activity.

[0055] The guidance unit can analyze the user's past travel history and suggest the optimal route. For example, it can suggest the optimal means of transportation based on routes the user has used in the past. It can also suggest routes that avoid congestion by taking into account routes the user has avoided in the past. Furthermore, it can suggest the most efficient means of transportation based on the means of transportation the user has used in the past. This makes it possible to suggest the optimal route based on the user's past travel history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives information from the user. Information from the user includes text information, voice information, image information, etc. For example, the reception unit can receive the user's information using voice input, and convert the user's voice information into text information using voice recognition technology and send it to the analysis unit. The reception unit can also receive the user's information using text input or image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's information using the generation AI and sends the results to the proposal unit. The generation AI is realized using technologies such as GPT-4 and Gemini. Step 3: The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestion unit uses a generation AI to make optimal suggestions to the user. For example, it can present recommendations to the user by restaurant menu item or display review information. Step 4: The guidance unit provides route guidance based on the information proposed by the suggestion unit. The guidance unit uses the generation AI to provide a map showing the route and information about the train to take. For example, it can provide guidance on the optimal route and transportation method from the user's current location to the destination.

[0058] (Example 2) The super AI app according to an embodiment of the present invention is a system that gently supports people who are not familiar with smartphones or who are tired of switching between multiple apps to gather information. For example, when craving a delicious lunch, conventionally, users would have to use multiple apps to gather information, decide on a restaurant, search for a route, and check the weather, which requires a lot of effort. However, with this super AI app, users can simply launch a single app and, through simple conversation, describe what they want to eat, and the app will recommend restaurant menu items. For example, by asking "What's it like?", reviews will appear. By asking "How about if we go now?", a map showing the route and train information will appear. All users need to do is follow the instructions. If they don't like the menu item, they can simply say, "I want something different in a similar category," and the app will make recommendations accordingly. This eliminates the need to use multiple apps and allows for a single app to handle everything from searching for what they want to eat, selecting a restaurant, providing route guidance, and providing review information, all in one place, based on simple user conversations. Even those unfamiliar with smartphones can easily use the super AI app.

[0059] The super AI app according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, text information, voice information, and image information. The reception unit can accept the user information using, for example, voice input. The reception unit can also accept the user information using text input. The reception unit can also accept the user information using image input. For example, the reception unit converts the user's voice information into text information using voice recognition technology and transmits it to the analysis unit. The analysis unit analyzes the information accepted by the reception unit. The analysis unit analyzes the user information using, for example, a generation AI. The generation AI is realized using, for example, technology such as GPT-4 or Gemini. The analysis unit analyzes the user information and transmits the results to the suggestion unit. The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestion unit makes optimal suggestions to the user using, for example, the generation AI. The suggestion unit, for example, presents recommendations to the user on a restaurant menu basis. The suggestion unit can also display review information to the user, for example, using a generation AI. The guidance unit provides route guidance based on the information suggested by the suggestion unit. The guidance unit, for example, uses a generation AI to provide a map showing the route and information on the train to take. The guidance unit, for example, guides the user to the optimal route from their current location to their destination. The guidance unit can also guide the user to the means of transportation from their current location to their destination, for example. This allows the super AI app according to the embodiment to efficiently accept, analyze, suggest, and guide the user's information.

[0060] The suggestion unit can use the generation AI to present recommendations by restaurant menu item. The suggestion unit uses the generation AI, for example, to suggest restaurant menu items that are optimal for the user. The generation AI is realized using technologies such as GPT-4 and Gemini. The suggestion unit presents recommendations based on the user's preferences and past usage history, for example. For example, if the user says, "I want to eat delicious ramen," the generation AI analyzes that information and searches for the optimal restaurant. Next, the suggestion unit presents recommendations by restaurant menu item based on the search results. For example, "I recommend the special ramen at this ramen restaurant." In this way, the generation AI can be used to suggest restaurant menu items that are optimal for the user.

[0061] The suggestion unit can display review information using a generation AI. The suggestion unit provides review information to users using, for example, a generation AI. The generation AI is realized using technologies such as GPT-4 and Gemini. For example, when a user asks, "What's it like?", the suggestion unit displays review information using the generation AI. For example, information such as "This ramen shop has high word-of-mouth reviews, and is particularly known for its delicious soup" is displayed. In this way, review information can be provided to users using the generation AI.

[0062] The guidance unit can provide a map showing the route or information about the train to take using the generation AI. The guidance unit provides optimal route guidance to the user using, for example, the generation AI. The generation AI is realized using technologies such as GPT-4 and Gemini. For example, when the user asks, "How about going now?", the generation AI provides a map showing the route and information about the train to take. For example, it might say, "It's a 10-minute walk to the nearest station, then get off the train two stops away, and then it's a 5-minute walk from there." In this way, the generation AI can provide optimal route guidance to the user.

[0063] The suggestion unit can use the generation AI to search again and present different recommendations. The suggestion unit provides different recommendations to the user, for example, using the generation AI. The generation AI is realized using technologies such as GPT-4 and Gemini. For example, if the user says, "I want something different in a similar category," the generation AI will search again and present a different recommendation. For example, the suggestion unit could say, "If you don't like the special ramen at this ramen shop, I recommend the soy sauce ramen at another ramen shop nearby." In this way, the generation AI can provide different recommendations to the user.

[0064] The reception unit can estimate a user's emotion and adjust the information reception method based on the estimated user's emotion. For example, the reception unit estimates a user's emotion and adjusts the information reception method based on the estimated user's emotion. The user's emotion is estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to quickly receive information. This allows for more appropriate information reception by adjusting the information reception method according to the user's emotion.

[0065] The reception unit can analyze the user's past usage history and select a reception method. The reception unit, for example, analyzes the user's past usage history and selects the optimal reception method. The past usage history includes, for example, past search history, purchase history, browsing history, etc. The reception unit, for example, automatically displays information that the user has frequently used in the past as a candidate. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past usage history. This makes it possible to provide the optimal reception method based on the user's past usage history.

[0066] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving information. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving information. The current situation includes, for example, a current location, a time zone, and weather. The areas of interest include, for example, past search keywords and browsing history. For example, when a user inputs their current situation, the reception unit preferentially displays related information. The reception unit can also filter and display highly relevant information based on the user's areas of interest. Furthermore, the reception unit can exclude unnecessary information and display only necessary information according to the user's current situation. This makes it possible to provide highly relevant information based on the user's current situation and areas of interest.

[0067] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of information to be received based on the estimated user emotions. The user's emotions are estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is feeling stressed, the reception unit prioritizes displaying important information and postpones unnecessary information. Furthermore, when the user is relaxed, the reception unit can prioritize displaying detailed information to expand options. Furthermore, when the user is in a hurry, the reception unit can display the most important information first to enable a quick response. In this way, by determining the priority of information according to the user's emotions, important information can be provided preferentially.

[0068] The reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving highly relevant information in consideration of the user's geographical location information when receiving information. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user inputs their current location, the reception unit prioritizes displaying nearby information. The reception unit can also filter and display highly relevant information based on the user's geographical location information. Furthermore, the reception unit can exclude unnecessary information and display only necessary information according to the user's current location. This makes it possible to provide highly relevant information based on the user's geographical location information.

[0069] The reception unit can analyze the user's social media activity when receiving information and receive related information. For example, the reception unit analyzes the user's social media activity when receiving information and receives related information. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit can analyze the user's social media activity and preferentially display related information, for example. The reception unit can also filter and display highly relevant information based on the user's areas of interest in social media. Furthermore, the reception unit can exclude unnecessary information and display only necessary information according to the user's social media activity. This makes it possible to provide highly relevant information based on the user's social media activity.

[0070] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. For example, 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 is evaluated based on criteria such as the user's level of interest and the urgency of the information. For example, the analysis unit performs a detailed analysis of important information to provide a deeper understanding. The analysis unit can also perform a concise analysis of general information to quickly provide results. Furthermore, the analysis unit can omit analysis of unnecessary information and provide only the necessary information. This makes it possible to provide analysis results with an appropriate level of detail based on the importance of the information.

[0072] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. Information categories include, for example, a news category, an entertainment category, and a technology category. Analysis algorithms include, for example, a clustering algorithm and a classification algorithm. For example, the analysis unit applies an analysis algorithm that emphasizes word-of-mouth reviews to restaurant information. The analysis unit can also apply an analysis algorithm that emphasizes real-time traffic conditions to traffic information. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes the latest weather forecast to weather information. This makes it possible to apply the optimal analysis algorithm depending on the category of information.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The user's emotions are estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0074] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of information during analysis. The time of submission of information includes, for example, the submission date and time, the submission order, etc. The analysis unit, for example, prioritizes analysis of the latest information and provides results quickly. The analysis unit can also lower the analysis priority of old information and perform analysis as needed. Furthermore, the analysis unit can also determine the priority of information whose submission time is unknown by comparing it with other information. This allows analysis to be performed with appropriate priority based on the time of submission of the information.

[0075] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. The relevance of information is evaluated based on criteria such as common keywords and related topics. For example, the analysis unit prioritizes analysis of highly relevant information and provides results quickly. The analysis unit can also postpone the analysis of less relevant information and perform analysis as needed. Furthermore, the analysis unit can determine the order of analysis for information whose relevance is unknown by comparing it with other information. This allows analysis to be performed in an appropriate order based on the relevance of the information.

[0076] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way in which suggestions are expressed based on the estimated user's emotion. The user's emotion is estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions to deepen understanding. Furthermore, when the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. Furthermore, when the user is excited, the suggestion unit can provide suggestions that include visually stimulating effects. In this way, by adjusting the way in which suggestions are expressed according to the user's emotion, more appropriate suggestions can be provided.

[0077] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the information when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the information when making a suggestion. The importance of the information is evaluated based on criteria such as the user's interest and the urgency of the information. For example, the suggestion unit makes detailed suggestions for important information to provide a deeper understanding. The suggestion unit can also make concise suggestions for general information to provide quick results. Furthermore, the suggestion unit can omit suggestions for unnecessary information and provide only the necessary information. This makes it possible to provide suggestions with an appropriate level of detail based on the importance of the information.

[0078] The suggestion unit can apply different suggestion algorithms depending on the category of information when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of information when making a suggestion. Information categories include, for example, a news category, an entertainment category, and a technology category. Proposal algorithms include, for example, a recommendation algorithm and a personalized algorithm. For example, the suggestion unit applies a suggestion algorithm that emphasizes word-of-mouth reviews to restaurant information. Furthermore, the suggestion unit can also apply a suggestion algorithm that emphasizes real-time traffic conditions to traffic information. Furthermore, the suggestion unit can apply a suggestion algorithm that emphasizes the latest weather forecast to weather information. This makes it possible to apply the optimal suggestion algorithm depending on the category of information.

[0079] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The user's emotion is estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the suggestion unit can provide a short and to-the-point suggestion when the user is in a hurry. The suggestion unit can also provide a longer suggestion with detailed explanations when the user is relaxed. Furthermore, the suggestion unit can provide a suggestion with visually stimulating effects when the user is excited. In this way, by adjusting the length of the suggestion according to the user's emotion, more appropriate suggestions can be provided.

[0080] The suggestion unit can determine the priority of the proposal based on the time of submission of the information at the time of proposal. For example, the suggestion unit determines the priority of the proposal based on the time of submission of the information at the time of proposal. The time of submission of the information includes, for example, the submission date and time, the submission order, etc. The suggestion unit, for example, gives priority to the latest information in making a proposal and provides results quickly. The suggestion unit can also lower the priority of the proposal for older information and make a proposal as needed. Furthermore, the suggestion unit can also determine the priority of information whose submission time is unknown by comparing it with other information. This allows suggestions to be made with appropriate priority based on the time of submission of the information.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of information when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of information when making suggestions. The relevance of information is evaluated based on criteria such as common keywords and related topics. For example, the suggestion unit prioritizes suggestions for highly relevant information and provides results quickly. The suggestion unit can also postpone the order of suggestions for less relevant information and make suggestions as needed. Furthermore, the suggestion unit can determine the order of suggestions for information whose relevance is unknown by comparing it with other information. This makes it possible to make suggestions in an appropriate order based on the relevance of information.

[0082] The guidance unit can estimate the user's emotions and adjust the guidance method based on the estimated user's emotions. The guidance unit, for example, estimates the user's emotions and adjusts the guidance method based on the estimated user's emotions. The user's emotions are estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the guidance unit can provide guidance in a calm voice. Also, if the user is relaxed, the guidance unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the guidance unit can provide quick and concise guidance. In this way, more appropriate guidance can be provided by adjusting the guidance method according to the user's emotions.

[0083] The guidance unit can analyze the user's past behavioral history and select the optimal guidance method when providing guidance. For example, the guidance unit analyzes the user's past behavioral history and selects the optimal guidance method when providing guidance. The past behavioral history includes, for example, past search history, purchase history, browsing history, etc. The guidance unit proposes the optimal guidance method, for example, based on routes the user has used in the past. The guidance unit can also propose a guidance method that avoids congestion based on the user's past behavioral history. Furthermore, the guidance unit can analyze the user's past behavioral history and propose the most efficient guidance method. This makes it possible to provide the optimal guidance method based on the user's past behavioral history.

[0084] The guidance unit can customize the guidance means based on the user's current situation when providing guidance. For example, the guidance unit customizes the guidance means based on the user's current situation when providing guidance. The current situation includes, for example, the current location, the time of day, the weather, etc. For example, if the user is in a hurry, the guidance unit can guide the user to the shortest route. Furthermore, if the user is relaxed, the guidance unit can also guide the user to a scenic route. Furthermore, if the user is tired, the guidance unit can guide the user to a route that includes rest points. This makes it possible to provide the optimal guidance means according to the user's current situation.

[0085] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated user emotions. The guidance unit, for example, estimates the user's emotions and determines the priority of guidance based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. For example, when the user is feeling stressed, the guidance unit prioritizes displaying important guidance and postpones unnecessary guidance. Furthermore, when the user is relaxed, the guidance unit can prioritize displaying detailed guidance to expand options. Furthermore, when the user is in a hurry, the guidance unit can display the most important guidance first to enable a quick response. In this way, by determining the priority of guidance according to the user's emotions, important guidance can be provided preferentially.

[0086] The guidance unit can select the optimal guidance method by taking into consideration the user's geographical location information when providing guidance. For example, the guidance unit selects the optimal guidance method by taking into consideration the user's geographical location information when providing guidance. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user inputs their current location, the guidance unit prioritizes displaying nearby information. The guidance unit can also filter and display highly relevant information based on the user's geographical location information. Furthermore, the guidance unit can exclude unnecessary information and display only necessary information according to the user's current location. This makes it possible to provide the optimal guidance method based on the user's geographical location information.

[0087] The guidance unit can analyze the user's social media activity and suggest guidance means when providing guidance. For example, the guidance unit analyzes the user's social media activity and suggests guidance means when providing guidance. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. For example, the guidance unit analyzes the user's social media activity and prioritizes displaying relevant information. The guidance unit can also filter and display highly relevant information based on the user's areas of interest in social media. Furthermore, the guidance unit can exclude unnecessary information and display only necessary information according to the user's social media activity. This makes it possible to provide optimal guidance means based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, suggestion unit, and guidance 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 reception unit accepts user voice information and text information using the microphone 38B or touch panel 38A 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 user information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and presents recommendations for each restaurant menu item based on the analysis results. The guidance unit is realized by the control unit 46A of the smart device 14 and provides a map showing the route and information on the train to take. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, analysis unit, suggestion unit, and guidance 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 reception unit receives the user's voice information using the microphone 238 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 user's information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and presents recommendations for each restaurant's menu item based on the analysis results. The guidance unit is realized by the control unit 46A of the smart glasses 214 and provides a map showing the route and information on the train to take. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and guidance unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives voice information from the user using the microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and presents recommendations for each restaurant's menu item based on the analysis results. The guidance unit is realized by the control unit 46A of the headset terminal 314 and provides a map showing the route and information on the train to take. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, suggestion unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice information from the user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and presents recommendations for each restaurant's menu item based on the analysis results. The guidance unit is realized by the control unit 46A of the robot 414 and provides a map showing the route and information about the train to take.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can analyze the user's tone of voice and speaking speed to estimate the user's level of urgency. For example, if the user is in a hurry, the reception unit can process information quickly and provide the necessary information in the shortest time possible. On the other hand, if the user is relaxed, the reception unit can provide detailed information, allowing the user to consider options at their own pace. Furthermore, if the user's tone of voice is unstable, the reception unit can provide an interface that gives a sense of security. This makes it possible to provide appropriate information according to the user's level of urgency.

[0090] The analysis unit can analyze a user's past search history and behavioral patterns to predict the information the user will need next. For example, if a user has frequently searched for specific information during a specific time period in the past, the analysis unit will prioritize analyzing information related to that time period. Also, if a user frequently searches for information in a specific category, the analysis unit can prioritize analyzing information related to that category. Furthermore, the analysis unit can predict the information the user will need next based on the user's behavioral patterns and perform analysis in advance. This allows the information the user needs to be provided quickly.

[0091] The suggestion unit can estimate the user's emotions and adjust the content of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest places and activities where the user can relax. If the user is excited, the suggestion unit can also suggest exciting events and activities. Furthermore, if the user is sad, the suggestion unit can make suggestions to lift the user's spirits. This makes it possible to make appropriate suggestions according to the user's emotions.

[0092] The guidance unit can adjust the guidance method taking into account the user's current health condition. For example, if the user is tired, the guidance unit can guide the user along a route that includes rest points. Also, if the user is in good health, the guidance unit can suggest active means of transportation such as walking or cycling. Furthermore, if the user has a specific health problem, the guidance unit can provide a guidance method that takes that problem into consideration. This enables optimal guidance according to the user's health condition.

[0093] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions to allow the user to carefully consider the suggestions. If the user is in a hurry, the suggestion unit can provide concise and quick suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This makes it possible to provide suggestions at appropriate times according to the user's emotions.

[0094] The reception unit monitors the user's device usage status and can receive information at the optimal timing. For example, if the user is using another app, the reception unit waits until the user finishes using that app. Also, if the user is using the device for a long time, the reception unit can display a message encouraging the user to take a break. Furthermore, if the user is not using the device, the reception unit can send a notification to encourage the user to receive information. This makes it possible to receive information at an appropriate timing according to the user's device usage status.

[0095] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed graphs and charts to enable the user to deeply understand the data. If the user is in a hurry, the analysis unit can provide a concise summary to enable the user to quickly grasp the information. Furthermore, if the user is excited, the analysis unit can provide the analysis results with visually appealing effects. This enables appropriate visual presentation according to the user's emotions.

[0096] The suggestion unit can analyze the user's social media activity and make suggestions based on the user's interests. For example, if the user frequently posts about a particular topic, the suggestion unit can prioritize suggesting information related to that topic. Also, if the user follows a particular account, the suggestion unit can suggest information related to that account. Furthermore, if the user uses a particular hashtag, the suggestion unit can suggest information related to that hashtag. This enables appropriate suggestions based on the user's social media activity.

[0097] The guidance unit can estimate the user's emotions and adjust the level of detail of the guidance based on the estimated user's emotions. For example, if the user is relaxed, the guidance unit can provide detailed guidance so that the user can carefully check the route. If the user is in a hurry, the guidance unit can provide concise and quick guidance. Furthermore, if the user is excited, the guidance unit can provide visually appealing guidance. This makes it possible to provide guidance with an appropriate level of detail according to the user's emotions.

[0098] The guidance unit can analyze the user's past travel history and suggest the optimal route. For example, it can suggest the optimal means of transportation based on routes the user has used in the past. It can also suggest routes that avoid congestion by taking into account routes the user has avoided in the past. Furthermore, it can suggest the most efficient means of transportation based on the means of transportation the user has used in the past. This makes it possible to suggest the optimal route based on the user's past travel history.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives information from the user. Information from the user includes text information, voice information, image information, etc. For example, the reception unit can receive the user's information using voice input, and convert the user's voice information into text information using voice recognition technology and send it to the analysis unit. The reception unit can also receive the user's information using text input or image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's information using the generation AI and sends the results to the proposal unit. The generation AI is realized using technologies such as GPT-4 and Gemini. Step 3: The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestion unit uses a generation AI to make optimal suggestions to the user. For example, it can present recommendations to the user by restaurant menu item or display review information. Step 4: The guidance unit provides route guidance based on the information proposed by the suggestion unit. The guidance unit uses the generation AI to provide a map showing the route and information about the train to take. For example, it can provide guidance on the optimal route and transportation method from the user's current location to the destination.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit; a proposal unit that makes a proposal based on the information analyzed by the analysis unit; a guidance unit that provides route guidance based on the information proposed by the proposal unit. A system characterized by:

2. The proposal unit Generative AI provides recommendations for each restaurant menu item 2. The system of claim 1.

3. The proposal unit Display review information using AI generation 2. The system of claim 1.

4. The guide unit is Generative AI provides route maps or train information 2. The system of claim 1.

5. The proposal unit Search again using generative AI to present different recommendations 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the method of receiving information based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past usage history and select the reception method 2. The system of claim 1.

8. The reception unit When receiving information, filter it based on the user's current situation and areas of interest.

2. The system of claim 1.

Images