system

Abstract

The system according to the embodiment aims to enable smartphone beginners and users who are tired of gathering information to easily obtain information. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a route calculation unit, and a display unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit and searches for restaurants and menus. The route calculation unit calculates a route based on the results obtained by the analysis unit, including weather and transportation options. The display unit displays the route calculated by the route calculation unit and provides information on menus, locations, and trains.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for smartphone beginners and users who are tired of gathering information to use multiple apps.

[0005] The system according to the embodiment aims to enable smartphone beginners and users who are tired of gathering information to easily obtain information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a route calculation unit, and a display unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit and searches for restaurants and menus. The route calculation unit calculates a route based on the results obtained by the analysis unit, including weather and transportation options. The display unit displays the route calculated by the route calculation unit and provides information on menus, locations, and trains. [Effects of the Invention]

[0007] The system according to the embodiment can enable smartphone beginners and users who are tired of gathering information to easily obtain information. [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 of an embodiment of the present invention is a system that gently supports smartphone beginners and those who are tired of gathering information. This system allows users to simply describe what they want to eat. The AI ​​searches for recommended restaurants and menus and displays routes that take weather and transportation into account. It also provides information on menus, locations, and trains, eliminating the need to use multiple apps. For example, if a user says, "I want ramen," the AI ​​analyzes that information and suggests nearby, highly rated ramen restaurants. It also displays routes that take weather and transportation into account, providing information on the suggested ramen restaurant's menu, location, and directions from the nearest station. This allows users to easily find recommended restaurants and menus without using multiple apps. This allows even smartphone beginners and those who are tired of gathering information to easily find recommended restaurants and menus. By displaying routes that take weather and transportation into account, the system also reduces the stress of traveling. It also provides information on menus, locations, and trains, eliminating the need to use multiple apps.

[0029] The super AI app according to the embodiment includes a reception unit, an analysis unit, a route calculation unit, and a display unit. The reception unit receives user input. Examples of user input include, but are not limited to, voice input, text input, and touch input. The reception unit can receive what the user says using, for example, voice recognition technology. The reception unit can also provide an interface for receiving text input. The reception unit can also include a touchscreen for receiving touch input. The analysis unit analyzes the information received by the reception unit and searches for restaurants and menus. The analysis unit can analyze the user input using, for example, natural language processing technology. The analysis unit can also use data mining technology to search for recommended restaurants and menus, taking into account the user's current location, past search history, ratings, and the like. The analysis unit can also use a machine learning algorithm to suggest optimal restaurants and menus based on the user's preferences. The route calculation unit calculates a route based on the weather and transportation information obtained by the analysis unit. The route calculation unit acquires weather and transportation information using, for example, weather data and a transportation API. The route calculation unit can also calculate a route based on criteria such as the shortest distance, the shortest time, or a combination of transportation modes. Furthermore, the route calculation unit can calculate an optimal route based on information about the user's current location and destination. The display unit displays the route calculated by the route calculation unit and provides menu, location, and train information. The display unit provides, for example, an interface for displaying map information. The display unit can also display text information and image information. Furthermore, the display unit can select the optimal display method depending on the user's device. As a result, the super AI app according to the embodiment can gently support smartphone beginners and those who are tired of gathering information by accepting and analyzing user input, calculating the optimal route, and providing information.

[0030] The reception unit can accept what the user says. The reception unit accepts what the user says using, for example, voice recognition technology. For example, if the user says, "I want to eat ramen," the reception unit recognizes the speech and converts it into text data. The reception unit can also analyze what the user says in real time and respond at an appropriate time. Furthermore, the reception unit can analyze the user's voice commands and execute specific operations. This allows the reception unit to respond to voice input by accepting what the user says. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0031] The analysis unit can search for restaurants and menus based on the user's current location, past search history, and ratings. The analysis unit, for example, acquires the user's current location and searches for restaurants and menus based on that information. The current location can be acquired using, for example, GPS data or Wi-Fi location information. The analysis unit can also analyze the user's past search history and suggest restaurants and menus based on the user's preferences. The past search history includes, for example, search keywords and search date and time. Furthermore, the analysis unit can also search for restaurants and menus taking into account the user's ratings. Ratings include, for example, user reviews and star ratings. This allows for more appropriate recommended restaurants and menus to be provided by taking into account the user's current location, past search history, ratings, and so on. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past search history data into the generation AI and cause the generation AI to suggest optimal restaurants and menus.

[0032] The route calculation unit can calculate a route based on the weather and the mode of transportation. The route calculation unit, for example, acquires weather data and calculates a route taking the weather into consideration. Weather data can be acquired using, for example, a weather forecast API. The route calculation unit can also acquire information on the mode of transportation and calculate an optimal route. Modes of transportation include, for example, public transportation, taxi, and walking. Furthermore, the route calculation unit can calculate a route based on criteria such as the shortest distance, the shortest time, and a combination of modes of transportation. This makes it possible to provide a more appropriate route by taking the weather and mode of transportation into consideration. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input weather data and information on the mode of transportation into a generation AI and have the generation AI calculate an optimal route.

[0033] The display unit can provide information such as the restaurant's menu, location, and directions from the nearest station. The display unit, for example, displays the menu of a proposed restaurant. The menu information source may be, for example, the restaurant's official website or a menu API. The display unit can also display the restaurant's location. The location information source may be, for example, map data or a location information service. The display unit can also display directions from the nearest station. The direction information source may be, for example, a map app or a navigation service. This eliminates the need for a user to use multiple apps by providing information such as the menu, location, and directions from the nearest station of the proposed restaurant. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input menu information of the proposed restaurant to a generation AI and cause the generation AI to display the menu.

[0034] The display unit can display a route based on the weather and the mode of transportation. The display unit can display a route that takes the weather into consideration, for example, based on weather data acquired using a weather forecast API. The display unit can also display an optimal route based on information on the mode of transportation acquired using a transportation API. Furthermore, the display unit can provide an interface for displaying map information so that the user can visually check the route. This can reduce the stress of traveling by displaying a route that takes the weather and the mode of transportation into consideration. Some or all of the above-described processing in the display unit can be performed, for example, using AI or without AI. For example, the display unit can input data acquired from the weather forecast API and the transportation API into a generation AI and cause the generation AI to display the optimal route.

[0035] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, stores the user's past input history in a database and analyzes the data. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0036] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. The reception unit, for example, acquires the user's current situation and performs filtering based on that information. The current situation includes, for example, location information, time of day, and weather. The reception unit can also identify the user's areas of interest and perform filtering based on that information. Areas of interest include, for example, past search history and social media activity. This makes it possible to provide more appropriate information by filtering based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation data to a generation AI and have the generation AI perform filtering.

[0037] When receiving input, the reception unit can select the optimal reception means depending on the user's input method. The reception unit, for example, detects the user's input method and selects the optimal reception means based on that information. Input methods include, for example, voice input, text input, and image input. For example, if a user says, "I want to eat ramen," the reception unit can prioritize receiving the voice input. Also, if a user inputs, "I want to eat ramen," in text, the reception unit can prioritize receiving the text input. Furthermore, if a user uploads a photo of ramen, the reception unit can prioritize receiving the image input. This allows for the selection of the optimal reception means depending on the user's input method, thereby providing a system that is easy for users to use. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0038] When receiving input, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information and prioritizes receiving highly relevant information based on that information. For example, GPS data or Wi-Fi location information can be used to acquire the geographical location information. For example, information about nearby shops can be prioritized based on the user's current location. Furthermore, if the user is in a specific area, the reception unit can prioritize providing information about popular shops in that area. Furthermore, if the user is traveling, the reception unit can prioritize providing information about shops near tourist spots. This allows highly relevant information to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0039] The reception unit can analyze the user's social media activity and receive related information when receiving input. The reception unit, for example, stores the user's social media activity in a database and analyzes the data. For example, the reception unit can provide information about stores related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and provide information about related stores. Furthermore, the reception unit can provide information about related stores by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related information.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit, for example, stores the user's past feedback in a database and analyzes the data. For example, the reception unit can prioritize providing information about stores that the user has previously rated highly. It can also exclude information about stores that the user has previously rated poorly. Furthermore, it can suggest the optimal reception method based on the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, evaluates the importance of the information and adjusts the level of detail of the analysis based on that information. The importance of the information includes, for example, the user's level of interest and the urgency of the information. For example, the analysis unit provides detailed analysis results for information with high importance. The analysis unit can also provide concise analysis results for information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit identifies the category of information and applies different analysis algorithms depending on the category. Information categories include, for example, text data, image data, and audio data. For example, for information in the food category, the analysis unit applies an analysis algorithm specialized for food. For information in the traffic category, the analysis unit can also apply an analysis algorithm specialized for traffic. Furthermore, for information in the weather category, the analysis unit can also apply an analysis algorithm specialized for weather. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, stores the user's past analysis results in a database and analyzes the data. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by excluding analysis results that the user has previously rated poorly. Furthermore, the optimal analysis algorithm can be selected based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of information submission. The analysis unit, for example, acquires the time of information submission and determines the analysis priority based on that information. The time of information submission includes, for example, the submission date and time and the submission frequency. For example, the most recent information may be analyzed preferentially and the results provided. Older information may also be analyzed with a lower priority. Furthermore, the analysis priority can be dynamically adjusted depending on the time of information submission. In this way, by determining the analysis priority based on the time of information submission, the most recent information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, evaluates the relevance of the information and adjusts the order of analysis based on that information. The relevance of the information includes, for example, co-occurrence relationships and correlation relationships. For example, highly relevant information is prioritized in the analysis and the results are provided. Also, less relevant information can be postponed in the analysis order. Furthermore, the analysis order can be dynamically adjusted according to the relevance of the information. In this way, by adjusting the analysis order based on the relevance of the information, highly relevant information can be prioritized in the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise and adjusts the use of technical terms in the analysis based on that information. The level of expertise includes, for example, the user's occupation and past learning history. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis unit can also provide analysis results that avoid technical terms. Furthermore, the use of technical terms in the analysis can be dynamically adjusted according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0047] The route calculation unit can improve the accuracy of route calculation by taking into account the interrelationships between information when calculating a route. The route calculation unit, for example, evaluates the interrelationships between information and improves the accuracy of route calculation based on that information. The interrelationships between information include, for example, co-occurrence relationships and correlation relationships. For example, the optimal route can be calculated by taking into account the interrelationships between weather information and traffic information. The optimal route can also be calculated by taking into account the interrelationships between the user's current location and destination. Furthermore, the optimal route can be calculated by taking into account the interrelationships between the user's past movement history and current conditions. In this way, the accuracy of route calculation can be improved by taking into account the interrelationships between information. Some or all of the above-mentioned processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input interrelationship data between information into a generation AI and cause the generation AI to improve the accuracy of route calculation.

[0048] The route calculation unit can calculate a route by taking into account the attribute information of the information submitter. The route calculation unit, for example, acquires the attribute information of the information submitter and calculates a route based on that information. The attribute information of the submitter includes, for example, age, gender, and occupation. For example, if the submitter is a local resident, that information is given priority when calculating a route. Also, if the submitter is a tourist, that information can be used to calculate a route that includes tourist spots. Furthermore, an optimal route can be calculated based on the attribute information of the submitter. In this way, by taking into account the attribute information of the information submitter, a more appropriate route can be provided. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input the attribute information data of the information submitter into a generation AI and have the generation AI perform route calculation.

[0049] When calculating a route, the route calculation unit can weight the route calculation based on the frequency of information submission. The route calculation unit, for example, evaluates the frequency of information submission and weights the route calculation based on that information. The frequency of information submission includes, for example, the number of submissions and the submission interval. For example, the route calculation can be performed by giving priority to information submitted more frequently. Also, the route calculation can be performed by lowering the weight of information submitted less frequently. Furthermore, the weighting of the route calculation can be dynamically adjusted according to the frequency of information submission. In this way, by weighting the route calculation based on the frequency of information submission, a more reliable route can be provided. Some or all of the above-mentioned processing in the route calculation unit can be performed using, for example, AI, or without AI. For example, the route calculation unit can input information submission frequency data to a generation AI and have the generation AI perform weighting in the route calculation.

[0050] The route calculation unit can calculate a route taking into account the geographic distribution of information. The route calculation unit, for example, evaluates the geographic distribution of information and calculates a route based on that information. The geographic distribution of information includes, for example, a geographic information system (GIS) and location information services. For example, the route calculation unit can prioritize geographically close information. Also, the route can be calculated by lowering the weighting of geographically distant information. Furthermore, the weighting of the route calculation can be dynamically adjusted according to the geographic distribution of information. In this way, a more appropriate route can be provided by taking the geographic distribution of information into consideration. Some or all of the above-described processing in the route calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the route calculation unit can input geographic distribution data of information to a generation AI and have the generation AI perform route calculation.

[0051] The route calculation unit can improve the accuracy of route calculation by referring to literature related to the information when calculating the route. The route calculation unit, for example, acquires literature related to the information and improves the accuracy of the route calculation based on that information. Related literature includes, for example, academic papers and technical reports. For example, the optimal route is calculated based on the related literature. The accuracy of the route calculation can also be improved by referring to information in the related literature. Furthermore, the route calculation algorithm can be improved by utilizing data from the related literature. In this way, the accuracy of the route calculation can be improved by referring to literature related to the information. Some or all of the above-mentioned processing in the route calculation unit can be performed, for example, using AI or without AI. For example, the route calculation unit can input literature data related to the information into a generation AI and cause the generation AI to improve the accuracy of the route calculation.

[0052] The route calculation unit can calculate a route taking into account the market value of the information. The route calculation unit, for example, evaluates the market value of the information and calculates a route based on that information. Market value includes, for example, price information and demand forecasts. For example, the route can be calculated by prioritizing information with high market value. Also, for information with low market value, a route can be calculated by lowering the weighting. Furthermore, the weighting of the route calculation can be dynamically adjusted according to the market value of the information. In this way, by taking the market value of the information into consideration, a more valuable route can be provided. Some or all of the above-mentioned processing in the route calculation unit can be performed, for example, using AI, or can be performed without using AI. For example, the route calculation unit can input market value data of the information into a generation AI and have the generation AI perform route calculation.

[0053] When displaying, the display unit can select the optimal display method by referring to the user's past operation history. The display unit, for example, stores the user's past operation history in a database and analyzes the data. For example, the display unit prioritizes providing a display method that the user has previously preferred. The display unit can also predict and provide an optimal display method from the user's past operation history. Furthermore, the display method can be customized based on the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0054] The display unit can customize the display content according to the user's current task. For example, the display unit identifies the user's current task and customizes the display content based on that information. The current task may include, for example, the user's schedule and current work. For example, if the user is currently looking for "lunch," the display unit may prioritize displaying information about restaurants suitable for lunchtime. If the user is interested in "Japanese food," the display unit may prioritize displaying information about Japanese restaurants. Furthermore, if the user is "health-conscious," the display unit may prioritize displaying information about restaurants offering healthy menus. This allows the display content to be customized according to the user's current task, providing more appropriate information. Some or all of the above-described processing by the display unit may be performed using, or without, AI. For example, the display unit may input the user's current task data into a generation AI and have the generation AI customize the display content.

[0055] The display unit can select the optimal display method by taking into account the user's device information when displaying. The display unit, for example, acquires the user's device information and selects the optimal display method based on that information. The device information includes, for example, the device type, screen size, and OS. For example, if the user is using a smartphone, the display unit can provide a display method tailored to the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple, highly visible display method. This allows the optimal display method to be provided by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0056] The display unit can select the optimal display method by taking into account the user's device information when displaying. The display unit, for example, acquires the user's device information and selects the optimal display method based on that information. The device information includes, for example, the device type, screen size, and OS. For example, if the user is using a smartphone, the display unit can provide a display method tailored to the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple, highly visible display method. This allows the optimal display method to be provided by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0057] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit, for example, acquires the language setting of the user's device and makes the display content multilingual based on that information. The language setting includes, for example, the device's language setting and the user's selected language. For example, the display content is automatically set based on the user's device's language setting. In addition, if the user uses multiple languages, the display unit can provide a language switching function. Furthermore, if the user selects a specific language, the display unit can provide the display content in that language. This allows the display content to be multilingual according to the user's language setting, thereby accommodating a wider range of users. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can input the user's language setting data into a generation AI and have the generation AI execute multilingual display content.

[0058] When displaying the information, the display unit can analyze the user's social media activity and provide related information. For example, the display unit stores the user's social media activity in a database and analyzes the data. For example, the display unit can provide information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the display unit can provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and cause the generation AI to provide related information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When accepting input from a user, the acceptance unit can analyze the user's past input history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the acceptance unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the acceptance unit can preferentially suggest text input. Furthermore, the acceptance unit can predict and suggest the input method to be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0061] When calculating a route based on weather and transportation, the route calculation unit can propose an optimal route by taking into account the user's past travel history. For example, it can analyze routes and transportation methods used by the user in the past and calculate an optimal route under similar conditions. It can also propose a different route by taking into account routes and transportation methods that the user has avoided in the past. Furthermore, it can propose a route suitable for a specific time period based on the user's past travel history. This makes it possible to provide a more appropriate route by taking into account the user's past travel history.

[0062] When displaying a route based on weather and transportation, the display unit can customize the display content according to the user's current task. For example, if the user's current task is looking for "lunch," the display unit can prioritize displaying information about restaurants suitable for lunchtime. Also, if the user is interested in "Japanese food," the display unit can prioritize displaying information about Japanese restaurants. Furthermore, if the user is "health-conscious," the display unit can prioritize displaying information about restaurants that offer healthy menus. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit evaluates the importance of the information and adjusts the level of detail of the analysis based on that information. The importance of the information includes, for example, the user's level of interest and the urgency of the information. For example, the analysis unit provides detailed analysis results for information with high importance. Furthermore, the analysis unit can also provide concise analysis results for information with low importance. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided.

[0064] When displaying, the display unit can select the optimal display method by taking into account the user's device information. For example, the display unit acquires the user's device information and selects the optimal display method based on that information. The device information includes, for example, the device type, screen size, and OS. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives user input. User input includes voice input, text input, touch input, etc. The reception unit can receive what the user says using voice recognition technology, and can also include an interface for receiving text input and a touch screen for receiving touch input. Step 2: The analysis unit analyzes the information received by the reception unit and searches for restaurants and menus. The analysis unit uses natural language processing and data mining technologies to analyze the user's input, and searches for recommended restaurants and menus taking into account the user's current location, past search history, ratings, etc. Furthermore, it can also use machine learning algorithms to suggest optimal restaurants and menus based on the user's preferences. Step 3: The route calculation unit calculates the route based on the results obtained by the analysis unit, including the weather and transportation method. The route calculation unit obtains weather and transportation information using meteorological data and a transportation API, and calculates the route based on criteria such as the shortest distance, shortest time, and combination of transportation methods. It can also calculate the optimal route based on the user's current location and destination information. Step 4: The display unit displays the route calculated by the route calculation unit and provides menu, location, and train information. The display unit provides an interface for displaying map information and can also display text and image information. Furthermore, it can select the optimal display method depending on the user's device.

[0067] (Example 2) The super AI app of an embodiment of the present invention is a system that gently supports smartphone beginners and those who are tired of gathering information. This system allows users to simply describe what they want to eat. The AI ​​searches for recommended restaurants and menus and displays routes that take weather and transportation into account. It also provides information on menus, locations, and trains, eliminating the need to use multiple apps. For example, if a user says, "I want ramen," the AI ​​analyzes that information and suggests nearby, highly rated ramen restaurants. It also displays routes that take weather and transportation into account, providing information on the suggested ramen restaurant's menu, location, and directions from the nearest station. This allows users to easily find recommended restaurants and menus without using multiple apps. This allows even smartphone beginners and those who are tired of gathering information to easily find recommended restaurants and menus. By displaying routes that take weather and transportation into account, the system also reduces the stress of traveling. It also provides information on menus, locations, and trains, eliminating the need to use multiple apps.

[0068] The super AI app according to the embodiment includes a reception unit, an analysis unit, a route calculation unit, and a display unit. The reception unit receives user input. Examples of user input include, but are not limited to, voice input, text input, and touch input. The reception unit can receive what the user says using, for example, voice recognition technology. The reception unit can also provide an interface for receiving text input. The reception unit can also include a touchscreen for receiving touch input. The analysis unit analyzes the information received by the reception unit and searches for restaurants and menus. The analysis unit can analyze the user input using, for example, natural language processing technology. The analysis unit can also use data mining technology to search for recommended restaurants and menus, taking into account the user's current location, past search history, ratings, and the like. The analysis unit can also use a machine learning algorithm to suggest optimal restaurants and menus based on the user's preferences. The route calculation unit calculates a route based on the weather and transportation information obtained by the analysis unit. The route calculation unit acquires weather and transportation information using, for example, weather data and a transportation API. The route calculation unit can also calculate a route based on criteria such as the shortest distance, the shortest time, or a combination of transportation modes. Furthermore, the route calculation unit can calculate an optimal route based on information about the user's current location and destination. The display unit displays the route calculated by the route calculation unit and provides menu, location, and train information. The display unit provides, for example, an interface for displaying map information. The display unit can also display text information and image information. Furthermore, the display unit can select the optimal display method depending on the user's device. As a result, the super AI app according to the embodiment can gently support smartphone beginners and those who are tired of gathering information by accepting and analyzing user input, calculating the optimal route, and providing information.

[0069] The reception unit can accept what the user says. The reception unit accepts what the user says using, for example, voice recognition technology. For example, if the user says, "I want to eat ramen," the reception unit recognizes the speech and converts it into text data. The reception unit can also analyze what the user says in real time and respond at an appropriate time. Furthermore, the reception unit can analyze the user's voice commands and execute specific operations. This allows the reception unit to respond to voice input by accepting what the user says. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0070] The analysis unit can search for restaurants and menus based on the user's current location, past search history, and ratings. The analysis unit, for example, acquires the user's current location and searches for restaurants and menus based on that information. The current location can be acquired using, for example, GPS data or Wi-Fi location information. The analysis unit can also analyze the user's past search history and suggest restaurants and menus based on the user's preferences. The past search history includes, for example, search keywords and search date and time. Furthermore, the analysis unit can also search for restaurants and menus taking into account the user's ratings. Ratings include, for example, user reviews and star ratings. This allows for more appropriate recommended restaurants and menus to be provided by taking into account the user's current location, past search history, ratings, and so on. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past search history data into the generation AI and cause the generation AI to suggest optimal restaurants and menus.

[0071] The route calculation unit can calculate a route based on the weather and the mode of transportation. The route calculation unit, for example, acquires weather data and calculates a route taking the weather into consideration. Weather data can be acquired using, for example, a weather forecast API. The route calculation unit can also acquire information on the mode of transportation and calculate an optimal route. Modes of transportation include, for example, public transportation, taxi, and walking. Furthermore, the route calculation unit can calculate a route based on criteria such as the shortest distance, the shortest time, and a combination of modes of transportation. This makes it possible to provide a more appropriate route by taking the weather and mode of transportation into consideration. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input weather data and information on the mode of transportation into a generation AI and have the generation AI calculate an optimal route.

[0072] The display unit can provide information such as the restaurant's menu, location, and directions from the nearest station. The display unit, for example, displays the menu of a proposed restaurant. The menu information source may be, for example, the restaurant's official website or a menu API. The display unit can also display the restaurant's location. The location information source may be, for example, map data or a location information service. The display unit can also display directions from the nearest station. The direction information source may be, for example, a map app or a navigation service. This eliminates the need for a user to use multiple apps by providing information such as the menu, location, and directions from the nearest station of the proposed restaurant. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input menu information of the proposed restaurant to a generation AI and cause the generation AI to display the menu.

[0073] The display unit can display a route based on the weather and the mode of transportation. The display unit can display a route that takes the weather into consideration, for example, based on weather data acquired using a weather forecast API. The display unit can also display an optimal route based on information on the mode of transportation acquired using a transportation API. Furthermore, the display unit can provide an interface for displaying map information so that the user can visually check the route. This can reduce the stress of traveling by displaying a route that takes the weather and the mode of transportation into consideration. Some or all of the above-described processing in the display unit can be performed, for example, using AI or without AI. For example, the display unit can input data acquired from the weather forecast API and the transportation API into a generation AI and cause the generation AI to display the optimal route.

[0074] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. The reception unit estimates the user's emotion using, for example, voice tone analysis technology. For example, if the user is stressed, the reception unit delays the timing of input reception to allow the user to enter information in a relaxed state. Furthermore, if the user is in a hurry, the reception unit can also accelerate the timing of input reception to quickly accept information. Furthermore, if the user is having fun, the reception unit can adjust the timing of input reception to allow the user to enter information while enjoying themselves. By adjusting the timing of input reception according to the user's emotion, the user can enter information in a relaxed state. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0075] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, stores the user's past input history in a database and analyzes the data. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0076] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. The reception unit, for example, acquires the user's current situation and performs filtering based on that information. The current situation includes, for example, location information, time of day, and weather. The reception unit can also identify the user's areas of interest and perform filtering based on that information. Areas of interest include, for example, past search history and social media activity. This makes it possible to provide more appropriate information by filtering based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation data to a generation AI and have the generation AI perform filtering.

[0077] When receiving input, the reception unit can select the optimal reception means depending on the user's input method. The reception unit, for example, detects the user's input method and selects the optimal reception means based on that information. Input methods include, for example, voice input, text input, and image input. For example, if a user says, "I want to eat ramen," the reception unit can prioritize receiving the voice input. Also, if a user inputs, "I want to eat ramen," in text, the reception unit can prioritize receiving the text input. Furthermore, if a user uploads a photo of ramen, the reception unit can prioritize receiving the image input. This allows for the selection of the optimal reception means depending on the user's input method, thereby providing a system that is easy for users to use. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0078] 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 estimates the user's emotions using, for example, voice tone analysis technology. For example, if the user is feeling stressed, the reception unit can prioritize providing information about restaurants where the user can relax. If the user is in a hurry, the reception unit can prioritize providing information about restaurants where the user can quickly eat. Furthermore, if the user is having fun, the reception unit can prioritize providing information about restaurants with a high entertainment value. This allows for more appropriate information to be provided by prioritizing information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0079] When receiving input, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information and prioritizes receiving highly relevant information based on that information. For example, GPS data or Wi-Fi location information can be used to acquire the geographical location information. For example, information about nearby shops can be prioritized based on the user's current location. Furthermore, if the user is in a specific area, the reception unit can prioritize providing information about popular shops in that area. Furthermore, if the user is traveling, the reception unit can prioritize providing information about shops near tourist spots. This allows highly relevant information to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0080] The reception unit can analyze the user's social media activity and receive related information when receiving input. The reception unit, for example, stores the user's social media activity in a database and analyzes the data. For example, the reception unit can provide information about stores related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and provide information about related stores. Furthermore, the reception unit can provide information about related stores by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related information.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit, for example, stores the user's past feedback in a database and analyzes the data. For example, the reception unit can prioritize providing information about stores that the user has previously rated highly. It can also exclude information about stores that the user has previously rated poorly. Furthermore, it can suggest the optimal reception method based on the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, voice tone analysis technology. For example, if the user is relaxed, the analysis unit can provide analysis results that proceed at a leisurely pace. 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 that add visually stimulating effects. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, evaluates the importance of the information and adjusts the level of detail of the analysis based on that information. The importance of the information includes, for example, the user's level of interest and the urgency of the information. For example, the analysis unit provides detailed analysis results for information with high importance. The analysis unit can also provide concise analysis results for information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit identifies the category of information and applies different analysis algorithms depending on the category. Information categories include, for example, text data, image data, and audio data. For example, for information in the food category, the analysis unit applies an analysis algorithm specialized for food. For information in the traffic category, the analysis unit can also apply an analysis algorithm specialized for traffic. Furthermore, for information in the weather category, the analysis unit can also apply an analysis algorithm specialized for weather. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, stores the user's past analysis results in a database and analyzes the data. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by excluding analysis results that the user has previously rated poorly. Furthermore, the optimal analysis algorithm can be selected based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0086] 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 estimates the user's emotions using, for example, voice tone analysis technology. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. Alternatively, 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. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time of information submission. The analysis unit, for example, acquires the time of information submission and determines the analysis priority based on that information. The time of information submission includes, for example, the submission date and time and the submission frequency. For example, the most recent information may be analyzed preferentially and the results provided. Older information may also be analyzed with a lower priority. Furthermore, the analysis priority can be dynamically adjusted depending on the time of information submission. In this way, by determining the analysis priority based on the time of information submission, the most recent information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, evaluates the relevance of the information and adjusts the order of analysis based on that information. The relevance of the information includes, for example, co-occurrence relationships and correlation relationships. For example, highly relevant information is prioritized in the analysis and the results are provided. Also, less relevant information can be postponed in the analysis order. Furthermore, the analysis order can be dynamically adjusted according to the relevance of the information. In this way, by adjusting the analysis order based on the relevance of the information, highly relevant information can be prioritized in the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise and adjusts the use of technical terms in the analysis based on that information. The level of expertise includes, for example, the user's occupation and past learning history. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis unit can also provide analysis results that avoid technical terms. Furthermore, the use of technical terms in the analysis can be dynamically adjusted according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0090] The route calculation unit can estimate the user's emotions and adjust the route calculation criteria based on the estimated user emotions. The route calculation unit estimates the user's emotions using, for example, audio tone analysis technology. For example, if the user is relaxed, the route calculation unit can prioritize calculating a scenic route. Furthermore, if the user is in a hurry, the route calculation unit can prioritize calculating the shortest route. Furthermore, if the user is excited, the route calculation unit can prioritize calculating a route that is more entertaining. This allows for adjusting the route calculation criteria according to the user's emotions to provide a more appropriate route. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the route calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the route calculation unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0091] The route calculation unit can improve the accuracy of route calculation by taking into account the interrelationships between information when calculating a route. The route calculation unit, for example, evaluates the interrelationships between information and improves the accuracy of route calculation based on that information. The interrelationships between information include, for example, co-occurrence relationships and correlation relationships. For example, the optimal route can be calculated by taking into account the interrelationships between weather information and traffic information. The optimal route can also be calculated by taking into account the interrelationships between the user's current location and destination. Furthermore, the optimal route can be calculated by taking into account the interrelationships between the user's past movement history and current conditions. In this way, the accuracy of route calculation can be improved by taking into account the interrelationships between information. Some or all of the above-mentioned processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input interrelationship data between information into a generation AI and cause the generation AI to improve the accuracy of route calculation.

[0092] The route calculation unit can calculate a route by taking into account the attribute information of the information submitter. The route calculation unit, for example, acquires the attribute information of the information submitter and calculates a route based on that information. The attribute information of the submitter includes, for example, age, gender, and occupation. For example, if the submitter is a local resident, that information is given priority when calculating a route. Also, if the submitter is a tourist, that information can be used to calculate a route that includes tourist spots. Furthermore, an optimal route can be calculated based on the attribute information of the submitter. In this way, by taking into account the attribute information of the information submitter, a more appropriate route can be provided. Some or all of the above-described processing in the route calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the route calculation unit can input the attribute information data of the information submitter into a generation AI and have the generation AI perform route calculation.

[0093] When calculating a route, the route calculation unit can weight the route calculation based on the frequency of information submission. The route calculation unit, for example, evaluates the frequency of information submission and weights the route calculation based on that information. The frequency of information submission includes, for example, the number of submissions and the submission interval. For example, the route calculation can be performed by giving priority to information submitted more frequently. Also, the route calculation can be performed by lowering the weight of information submitted less frequently. Furthermore, the weighting of the route calculation can be dynamically adjusted according to the frequency of information submission. In this way, by weighting the route calculation based on the frequency of information submission, a more reliable route can be provided. Some or all of the above-mentioned processing in the route calculation unit can be performed using, for example, AI, or without AI. For example, the route calculation unit can input information submission frequency data to a generation AI and have the generation AI perform weighting in the route calculation.

[0094] The route calculation unit can estimate the user's emotions and adjust the order in which route calculation results are displayed based on the estimated user's emotions. The route calculation unit estimates the user's emotions using, for example, audio tone analysis technology. For example, if the user is relaxed, the route calculation unit can prioritize displaying scenic routes. Furthermore, if the user is in a hurry, the route calculation unit can prioritize displaying the shortest route. Furthermore, if the user is excited, the route calculation unit can prioritize displaying highly entertaining routes. This allows for more appropriate information to be provided by adjusting the order in which route calculation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the route calculation unit can be performed using, for example, an AI. For example, the route calculation unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0095] The route calculation unit can calculate a route taking into account the geographic distribution of information. The route calculation unit, for example, evaluates the geographic distribution of information and calculates a route based on that information. The geographic distribution of information includes, for example, a geographic information system (GIS) and location information services. For example, the route calculation unit can prioritize geographically close information. Also, the route can be calculated by lowering the weighting of geographically distant information. Furthermore, the weighting of the route calculation can be dynamically adjusted according to the geographic distribution of information. In this way, a more appropriate route can be provided by taking the geographic distribution of information into consideration. Some or all of the above-described processing in the route calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the route calculation unit can input geographic distribution data of information to a generation AI and have the generation AI perform route calculation.

[0096] The route calculation unit can improve the accuracy of route calculation by referring to literature related to the information when calculating the route. The route calculation unit, for example, acquires literature related to the information and improves the accuracy of the route calculation based on that information. Related literature includes, for example, academic papers and technical reports. For example, the optimal route is calculated based on the related literature. The accuracy of the route calculation can also be improved by referring to information in the related literature. Furthermore, the route calculation algorithm can be improved by utilizing data from the related literature. In this way, the accuracy of the route calculation can be improved by referring to literature related to the information. Some or all of the above-mentioned processing in the route calculation unit can be performed, for example, using AI or without AI. For example, the route calculation unit can input literature data related to the information into a generation AI and cause the generation AI to improve the accuracy of the route calculation.

[0097] The route calculation unit can calculate a route taking into account the market value of the information. The route calculation unit, for example, evaluates the market value of the information and calculates a route based on that information. Market value includes, for example, price information and demand forecasts. For example, the route can be calculated by prioritizing information with high market value. Also, for information with low market value, a route can be calculated by lowering the weighting. Furthermore, the weighting of the route calculation can be dynamically adjusted according to the market value of the information. In this way, by taking the market value of the information into consideration, a more valuable route can be provided. Some or all of the above-mentioned processing in the route calculation unit can be performed, for example, using AI, or can be performed without using AI. For example, the route calculation unit can input market value data of the information into a generation AI and have the generation AI perform route calculation.

[0098] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. The display unit estimates the user's emotions using, for example, audio tone analysis technology. For example, if the user is relaxed, the display unit can provide a display method that proceeds at a leisurely pace. If the user is in a hurry, the display unit can provide a concise display method that focuses on the main points. Furthermore, if the user is excited, the display unit can provide a display method that adds visually stimulating effects. This allows for adjusting the display method according to the user's emotions to provide a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0099] When displaying, the display unit can select the optimal display method by referring to the user's past operation history. The display unit, for example, stores the user's past operation history in a database and analyzes the data. For example, the display unit prioritizes providing a display method that the user has previously preferred. The display unit can also predict and provide an optimal display method from the user's past operation history. Furthermore, the display method can be customized based on the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0100] The display unit can customize the display content according to the user's current task. For example, the display unit identifies the user's current task and customizes the display content based on that information. The current task may include, for example, the user's schedule and current work. For example, if the user is currently looking for "lunch," the display unit may prioritize displaying information about restaurants suitable for lunchtime. If the user is interested in "Japanese food," the display unit may prioritize displaying information about Japanese restaurants. Furthermore, if the user is "health-conscious," the display unit may prioritize displaying information about restaurants offering healthy menus. This allows the display content to be customized according to the user's current task, providing more appropriate information. Some or all of the above-described processing by the display unit may be performed using, or without, AI. For example, the display unit may input the user's current task data into a generation AI and have the generation AI customize the display content.

[0101] The display unit can select the optimal display method by taking into account the user's device information when displaying. The display unit, for example, acquires the user's device information and selects the optimal display method based on that information. The device information includes, for example, the device type, screen size, and OS. For example, if the user is using a smartphone, the display unit can provide a display method tailored to the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple, highly visible display method. This allows the optimal display method to be provided by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0102] The display unit can estimate the user's emotions and adjust the displayed operation procedures based on the estimated user's emotions. The display unit estimates the user's emotions using, for example, audio tone analysis technology. For example, if the user is relaxed, the display unit can provide operation procedures that proceed at a leisurely pace. If the user is in a hurry, the display unit can provide concise operation procedures that focus on the main points. Furthermore, if the user is excited, the display unit can provide operation procedures that add visually stimulating effects. This allows the display unit to adjust the displayed operation procedures according to the user's emotions and provide more appropriate operation procedures. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0103] The display unit can select the optimal display method by taking into account the user's device information when displaying. The display unit, for example, acquires the user's device information and selects the optimal display method based on that information. The device information includes, for example, the device type, screen size, and OS. For example, if the user is using a smartphone, the display unit can provide a display method tailored to the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple, highly visible display method. This allows the optimal display method to be provided by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0104] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit, for example, acquires the language setting of the user's device and makes the display content multilingual based on that information. The language setting includes, for example, the device's language setting and the user's selected language. For example, the display content is automatically set based on the user's device's language setting. In addition, if the user uses multiple languages, the display unit can provide a language switching function. Furthermore, if the user selects a specific language, the display unit can provide the display content in that language. This allows the display content to be multilingual according to the user's language setting, thereby accommodating a wider range of users. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can input the user's language setting data into a generation AI and have the generation AI execute multilingual display content.

[0105] When displaying the information, the display unit can analyze the user's social media activity and provide related information. For example, the display unit stores the user's social media activity in a database and analyzes the data. For example, the display unit can provide information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the display unit can provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, route calculation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user input and searches for shops and menus. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route based on weather and transportation. The display unit is realized, for example, by the display 40A of the smart device 14, and displays the calculated route, menu, location, and train information. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, route calculation unit, and display unit 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 user input using the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user input and searches for shops and menus. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route based on weather and transportation. The display unit is realized, for example, by the display of the smart glasses 214, and displays the calculated route, menu, location, and train information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, route calculation unit, and display 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 user input using the microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user input and searches for shops and menus. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route based on weather and transportation. The display unit is realized, for example, by the display 343 of the headset terminal 314, and displays the calculated route, menus, locations, and train information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, route calculation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user input and searches for shops and menus. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route based on weather and transportation. The display unit is realized, for example, by the display and speaker 240 of the robot 414, and displays the calculated route, menu, location, and train information.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When accepting input from a user, the acceptance unit can analyze the user's past input history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the acceptance unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the acceptance unit can preferentially suggest text input. Furthermore, the acceptance unit can predict and suggest the input method to be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0108] When searching for restaurants and menus, the analysis unit can estimate the user's emotions based on the user's current location, past search history, and ratings, and adjust the search results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can prioritize suggesting restaurants where the user can relax. Also, if the user is in a hurry, the analysis unit can prioritize suggesting restaurants where the user can eat quickly. Furthermore, if the user is having fun, the analysis unit can prioritize suggesting restaurants with a high entertainment value. This allows for more appropriate suggestions by adjusting the search results according to the user's emotions.

[0109] When calculating a route based on weather and transportation, the route calculation unit can propose an optimal route by taking into account the user's past travel history. For example, it can analyze routes and transportation methods used by the user in the past and calculate an optimal route under similar conditions. It can also propose a different route by taking into account routes and transportation methods that the user has avoided in the past. Furthermore, it can propose a route suitable for a specific time period based on the user's past travel history. This makes it possible to provide a more appropriate route by taking into account the user's past travel history.

[0110] The display unit can estimate the user's emotions when providing information about a restaurant's menu, location, or directions from the nearest station, and adjust the display content based on the estimated emotions. For example, if the user is relaxed, the display unit can provide a display method that proceeds at a leisurely pace. If the user is in a hurry, the display unit can provide a concise display method that focuses on the main points. Furthermore, if the user is excited, the display unit can provide a display method that adds visually stimulating effects. This makes it possible to provide more appropriate information by adjusting the display content according to the user's emotions.

[0111] When displaying a route based on weather and transportation, the display unit can customize the display content according to the user's current task. For example, if the user's current task is looking for "lunch," the display unit can prioritize displaying information about restaurants suitable for lunchtime. Also, if the user is interested in "Japanese food," the display unit can prioritize displaying information about Japanese restaurants. Furthermore, if the user is "health-conscious," the display unit can prioritize displaying information about restaurants that offer healthy menus. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0112] The reception unit can estimate the user's emotions and adjust the timing of input reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of input reception to allow the user to input in a relaxed state. Also, if the user is in a hurry, the reception unit can advance the timing of input reception to allow the user to input information quickly. Furthermore, if the user is having fun, the reception unit can adjust the timing of input reception to allow the user to input while enjoying themselves. In this way, by adjusting the timing of input reception according to the user's emotions, the user can input in a relaxed state.

[0113] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit evaluates the importance of the information and adjusts the level of detail of the analysis based on that information. The importance of the information includes, for example, the user's level of interest and the urgency of the information. For example, the analysis unit provides detailed analysis results for information with high importance. Furthermore, the analysis unit can also provide concise analysis results for information with low importance. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided.

[0114] The route calculation unit can estimate the user's emotions and adjust the route calculation criteria based on the estimated user's emotions. For example, if the user is relaxed, the route calculation unit can prioritize calculating a route with a good view. Alternatively, if the user is in a hurry, the route calculation unit can prioritize calculating the shortest route. Furthermore, if the user is excited, the route calculation unit can prioritize calculating a route that is more entertaining. In this way, by adjusting the route calculation criteria according to the user's emotions, a more appropriate route can be provided.

[0115] The display unit can estimate the user's emotions and adjust the displayed operation procedures based on the estimated user's emotions. For example, if the user is relaxed, the display unit can provide operation procedures that proceed at a leisurely pace. If the user is in a hurry, the display unit can provide concise operation procedures that focus on the main points. Furthermore, if the user is excited, the display unit can provide operation procedures that include visually stimulating effects. In this way, by adjusting the displayed operation procedures according to the user's emotions, more appropriate operation procedures can be provided.

[0116] When displaying, the display unit can select the optimal display method by taking into account the user's device information. For example, the display unit acquires the user's device information and selects the optimal display method based on that information. The device information includes, for example, the device type, screen size, and OS. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives user input. User input includes voice input, text input, touch input, etc. The reception unit can receive what the user says using voice recognition technology, and can also include an interface for receiving text input and a touch screen for receiving touch input. Step 2: The analysis unit analyzes the information received by the reception unit and searches for restaurants and menus. The analysis unit uses natural language processing and data mining technologies to analyze the user's input, and searches for recommended restaurants and menus taking into account the user's current location, past search history, ratings, etc. Furthermore, it can also use machine learning algorithms to suggest optimal restaurants and menus based on the user's preferences. Step 3: The route calculation unit calculates the route based on the results obtained by the analysis unit, including the weather and transportation method. The route calculation unit obtains weather and transportation information using meteorological data and a transportation API, and calculates the route based on criteria such as the shortest distance, shortest time, and combination of transportation methods. It can also calculate the optimal route based on the user's current location and destination information. Step 4: The display unit displays the route calculated by the route calculation unit and provides menu, location, and train information. The display unit provides an interface for displaying map information and can also display text and image information. Furthermore, it can select the optimal display method depending on the user's device.

[0119] 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.

[0120] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

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

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 input from a user; an analysis unit that analyzes the information received by the reception unit and searches for shops and menus; a route calculation unit that calculates a route based on the results obtained by the analysis unit and on weather and transportation means; a display unit that displays the route calculated by the route calculation unit and provides menu, location, and train information. A system characterized by:

2. The reception unit Accept what the user says 2. The system of claim 1.

3. The analysis unit Search for restaurants and menus based on the user's current location, past search history, and ratings 2. The system of claim 1.

4. The route calculation unit Calculate routes based on weather and transportation 2. The system of claim 1.

5. The display unit Providing information about the restaurant's menu, location, and how to get there from the nearest station 2. The system of claim 1.

6. The display unit Show routes based on weather and transportation 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

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