system

Abstract

The system according to this embodiment aims to provide optimal maps without requiring users to manually configure settings or register information. [Solution] The system according to the embodiment comprises an acquisition unit, a history acquisition unit, a generation unit, and a provision unit. The acquisition unit acquires the current location status. The history acquisition unit acquires past activity history. The generation unit generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit. The provision unit provides the map generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user has to manually perform settings and information registration related to the map, and there is room for improvement in convenience.

[0005] The system according to the embodiment aims to provide an optimal map without the user manually performing settings and information registration.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, a history acquisition unit, a generation unit, and a provision unit. The acquisition unit acquires the current situation. The history acquisition unit acquires the past behavior history. The generation unit generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit. The provision unit provides the map generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal map without requiring the user to manually configure settings or register information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The map optimization system according to an embodiment of the present invention is a system that eliminates the need for users to manually perform various map settings and register information. The map optimization system improves convenience by having AI make real-time decisions based on the user's current location and past activity history, and providing this information to the user. For example, the map optimization system acquires the user's current location. For example, it acquires information such as whether it is a city, regional city, or tourist destination, and whether it is crowded, normal, or quiet. The map optimization system also acquires unique information about the area. Next, the map optimization system acquires past activity history. For example, it acquires the range of activity obtained by acquiring latitude and longitude, search history from smartphones and personal computers, and past map usage status. Based on this information, the map optimization system generates an optimal map in real time. Specifically, it optimizes the type and scale of the map, the content and amount of information displayed for facilities, and important accompanying information. For example, in a crowded urban area, major facilities and transportation information are highlighted, and in a tourist destination, information on tourist spots and restaurants is displayed in detail. This mechanism eliminates the need for users to manually set up maps and register information. Furthermore, the map optimization system can provide map information that reflects the latest conditions through real-time information updates. This improves user convenience and allows users to reach their destinations more efficiently. In addition, the map optimization system caters to a wide range of target audiences, including general users who use map apps daily, professionals such as delivery drivers and taxi drivers, and travelers and tourists. For example, delivery drivers can significantly improve their operational efficiency by being automatically provided with the optimal route. Travelers can also smoothly plan their trips by obtaining the latest information on tourist destinations in real time. Thus, the map optimization system not only improves user convenience but also contributes to improved operational efficiency, real-time response, and the realization of a sustainable society. For example, by providing efficient routes, it contributes to reducing fuel consumption and CO2 emissions, aiming for the realization of a sustainable society. In this way, the map optimization system can provide the optimal map in real time based on the user's current location and past activity history.

[0029] The map optimization system according to this embodiment comprises an acquisition unit, a history acquisition unit, a generation unit, and a provision unit. The acquisition unit acquires the user's current location information. The acquisition unit acquires information such as urban areas, regional cities, and tourist destinations. The acquisition unit can also acquire information such as congestion, normal conditions, and quiet areas. Furthermore, the acquisition unit can acquire unique information about the area. For example, the acquisition unit acquires information on urban population density and major facilities. The acquisition unit can also acquire information on tourist spots and restaurants in tourist destinations. The history acquisition unit acquires the user's past activity history. For example, the history acquisition unit acquires the range of activity by acquiring latitude and longitude. The history acquisition unit can also acquire search history from smartphones and personal computers. Furthermore, the history acquisition unit can acquire past map usage information. For example, the history acquisition unit acquires places the user has visited in the past and means of transportation. The generation unit generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit. The generation unit optimizes the map type and scale, for example. The generation unit can also optimize the display content and amount of facilities. Furthermore, the generation unit can optimize associated important information. For example, in congested urban areas, the generation unit can highlight key facilities and transportation information. In tourist areas, the generation unit can also display detailed information on tourist attractions and restaurants. The provisioning unit provides the map generated by the generation unit. The provisioning unit, for example, displays the map on the user's device. The provisioning unit can also provide map update information in real time. Furthermore, the provisioning unit can provide map information tailored to the user's needs. For example, the provisioning unit can provide the optimal route to a delivery company. The provisioning unit can also provide travelers with the latest information on tourist destinations. As a result, the map optimization system according to this embodiment can provide the optimal map in real time based on the user's current location and past activity history.

[0030] The data acquisition unit obtains information about the user's current location. Specifically, it collects GPS data from the user's device to determine their current location. Furthermore, the unit acquires information such as whether the location is a city, a regional city, or a tourist destination. For example, in cities, it acquires information on population density and major facilities, while in regional cities, it collects information on local specialties and events. In tourist destinations, it can acquire information on tourist spots and restaurants. This allows the data acquisition unit to understand detailed information about the user's current location. The data acquisition unit can also acquire information on conditions such as congestion, normal, and quiet. For example, it can analyze real-time pedestrian flow data to understand the congestion level in a specific area. This can provide useful information for users who want to avoid crowds or are looking for quiet places. Furthermore, the data acquisition unit can acquire unique information about a particular location. For example, it can acquire information on population density and major facilities in cities. It can also acquire information on tourist spots and restaurants in tourist destinations. This allows the unit to provide detailed information about places the user visits, supporting a more fulfilling experience. The data acquisition unit centrally manages this information and can collaborate with other systems and departments as needed. For example, the acquired data is stored on a cloud server, making it accessible to the history acquisition and generation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The history acquisition unit retrieves the user's past activity history. Specifically, it collects latitude and longitude data from the user's device to identify the user's past range of activity. For example, it can obtain information on places the user has visited and the means of transportation they have used in the past. This allows the system to understand the user's behavior patterns and provide map information tailored to their individual needs. The history acquisition unit can also acquire search history from smartphones and personal computers. For example, it can collect information on tourist spots and restaurants the user has searched for in the past and generate an optimal map based on this information. Furthermore, the history acquisition unit can acquire information on past map usage. For example, it can analyze the types of maps the user has used and the content displayed in the past to provide a map tailored to the user's preferences. This allows the history acquisition unit to gain a detailed understanding of the user's past activity history and provide optimal map information tailored to their individual needs. In addition, the history acquisition unit can centrally manage this data and collaborate with other systems and departments as needed. For example, data collected by the history acquisition unit can be stored on a cloud server and made accessible to the generation unit. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the history acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0032] The generation unit generates the optimal map based on the information acquired by the acquisition unit and the history acquisition unit. Specifically, the generation unit considers the user's current location and past activity history to optimize the map type and scale. For example, in congested urban areas, it can highlight major facilities and transportation information, and in tourist areas, it can display detailed information on tourist spots and restaurants. The generation unit can also optimize the content and amount of information displayed for facilities. For example, it can prioritize displaying facilities and spots of interest to the user and omit unnecessary information to provide a map that is easy to view and use. Furthermore, the generation unit can optimize accompanying important information. For example, it can display current weather information, traffic conditions, and event information in real time to quickly provide the information the user needs. The generation unit centrally manages this information and can cooperate with other systems and departments as needed. For example, the generated maps are stored on a cloud server and made accessible to the provisioning unit. In addition, by adjusting the frequency and accuracy of map generation, flexible responses to specific situations and conditions are possible. As a result, the generation unit can generate maps efficiently and effectively, improving the overall performance of the system.

[0033] The service provider provides maps generated by the generation unit. Specifically, the service provider displays maps on the user's device. For example, it displays maps optimized for devices such as smartphones, tablets, and personal computers, making them easily accessible to users. The service provider can also provide map updates in real time. For example, it can update current traffic conditions, weather information, and event information in real time, ensuring users always have access to the latest information. Furthermore, the service provider can provide map information tailored to user needs. For example, it can provide optimal routes for delivery companies and the latest information on tourist destinations for travelers. This allows the service provider to quickly and accurately provide map information that meets the diverse needs of users. In addition, the service provider can collect user feedback and continuously improve the accuracy and content of the maps. For example, it can improve the accuracy of map display and route guidance based on user feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide map information to users, improving user convenience.

[0034] The data acquisition unit can acquire information on cities, regional cities, tourist destinations, etc. For example, the data acquisition unit can acquire information on the population density and major facilities of cities. It can also acquire information on traffic conditions and public facilities in regional cities. Furthermore, the data acquisition unit can acquire information on tourist spots and restaurants in tourist destinations. For example, the data acquisition unit can collect information on major transportation and commercial facilities in cities. It can also collect information on public transportation and major tourist spots in regional cities. Furthermore, the data acquisition unit can collect information on popular tourist spots and restaurants in tourist destinations. As a result, the data acquisition unit can acquire information tailored to the user's current location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input urban information into AI, and the AI ​​can analyze and acquire the information.

[0035] The history acquisition unit can acquire the user's range of activity by obtaining latitude and longitude coordinates. For example, the history acquisition unit can acquire the user's range of activity using GPS data. The history acquisition unit can also acquire the user's range of activity using location information services. Furthermore, the history acquisition unit can acquire the user's means of transportation and duration of stay. For example, the history acquisition unit collects latitude and longitude data of places the user has visited in the past. The history acquisition unit can also collect the means of transportation the user has used in the past and duration of stay. Furthermore, the history acquisition unit can analyze the user's behavior patterns and identify their range of activity. This allows the history acquisition unit to acquire the user's past range of activity. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input GPS data into AI, and the AI ​​can analyze the data to identify the range of activity.

[0036] The generation unit can optimize the type and scale of the map. For example, it can optimize the type of map, such as road maps or tourist maps. It can also optimize the scale, such as 1:1000 or 1:5000. Furthermore, the generation unit can adjust the type and scale of the map according to the user's needs. For example, in congested urban areas, the generation unit can highlight major facilities and transportation information. In tourist areas, the generation unit can display detailed information on tourist spots and restaurants. Furthermore, in regional cities, the generation unit can display information on public transportation and major tourist spots. In this way, the generation unit can generate the optimal map according to the user's needs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the type and scale of the map into the AI, and the AI ​​can generate the optimal map.

[0037] The information provider can optimize the content and amount of information displayed for each facility. For example, it can optimize the types of facilities to display. It can also optimize the level of detail of the information displayed. Furthermore, it can adjust the content and amount of information displayed for each facility according to the user's needs. For example, in congested urban areas, the information provider can highlight major facilities and transportation information. In tourist areas, it can also display detailed information on tourist spots and restaurants. Furthermore, in regional cities, it can display information on public transportation and major tourist spots. This allows the information provider to provide users with the information they need in the most optimal way. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the content and amount of information to be displayed for each facility into the AI, which can then determine the optimal display method.

[0038] The acquisition unit can acquire weather information for the current location and provide map information appropriate to the weather. For example, in rainy weather, the acquisition unit can prioritize displaying routes where umbrellas can be used or routes with roofs. In sunny weather, the acquisition unit can also prioritize displaying routes with good scenery or routes that pass through parks. Furthermore, on snowy days, the acquisition unit can prioritize displaying routes that are less slippery or routes that have been cleared of snow. In this way, the acquisition unit can provide optimal map information appropriate to the weather. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input weather data into AI, and the AI ​​can generate the optimal route appropriate to the weather.

[0039] The acquisition unit can acquire real-time traffic conditions at the current location and provide map information according to the degree of congestion. For example, if traffic congestion occurs, the acquisition unit can suggest an alternative route. The acquisition unit can also suggest an alternative route based on delay information for public transportation. Furthermore, if a traffic accident occurs, the acquisition unit can display that information on the map and warn the user. In this way, the acquisition unit can provide optimal map information according to the traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input traffic data into AI, and the AI ​​can generate an optimal route according to the degree of congestion.

[0040] The acquisition unit can acquire event information for the current location and provide map information corresponding to the event. For example, the acquisition unit can acquire information on events being held at the current location and display a route to the event venue. The acquisition unit can also acquire information on festivals and markets being held at the current location and display related facilities. Furthermore, the acquisition unit can acquire information on sporting events being held at the current location and display a route to the viewing location. In this way, the acquisition unit can provide optimal map information according to the event information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input event information into AI, and the AI ​​can generate an optimal route according to the event.

[0041] The acquisition unit can acquire safety information (such as crime rates) for the current location and provide map information tailored to the level of safety. For example, the acquisition unit can suggest a route that avoids areas with high crime rates. It can also highlight safe areas and suggest routes that allow for safe travel. Furthermore, the acquisition unit can display evacuation locations on the map in case of emergency and suggest evacuation routes. In this way, the acquisition unit can provide optimal map information tailored to the safety information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input safety information into AI, which can then generate an optimal route tailored to the level of safety.

[0042] The history acquisition unit can acquire the user's past modes of transportation and provide map information corresponding to those modes. For example, the history acquisition unit can suggest a route suitable for walking based on routes the user has traveled on foot in the past. It can also suggest a route suitable for cycling based on routes the user has traveled by bicycle in the past. Furthermore, it can suggest a route suitable for driving based on routes the user has traveled by car in the past. In this way, the history acquisition unit can provide optimal map information according to the mode of transportation. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's mode of transportation data into AI, and the AI ​​can generate an optimal route according to the mode of transportation.

[0043] The history acquisition unit can acquire the user's past visit frequency and provide map information according to that frequency. For example, the history acquisition unit can highlight places the user has frequently visited in the past and suggest routes. The history acquisition unit can also suggest related places based on places the user has visited only once in the past. Furthermore, the history acquisition unit can suggest the optimal route based on the visit frequency of places the user has visited in the past. In this way, the history acquisition unit can provide optimal map information according to the visit frequency. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's visit frequency data into AI, and the AI ​​can generate the optimal route according to the frequency.

[0044] The history acquisition unit can acquire a user's past purchase history and provide map information corresponding to that purchase history. For example, the history acquisition unit can display stores related to products the user has previously purchased on a map. The history acquisition unit can also display relevant events and sales information on a map based on the user's purchase history. Furthermore, the history acquisition unit can analyze the user's purchase history and suggest the optimal shopping route. In this way, the history acquisition unit can provide optimal map information according to the purchase history. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's purchase history data into AI, and the AI ​​can generate the optimal route according to the purchase history.

[0045] The history acquisition unit can acquire a user's past social media activity and provide map information corresponding to that activity. For example, the history acquisition unit can display on a map the locations where the user has previously checked in. The history acquisition unit can also suggest relevant locations based on the content of the user's social media posts. Furthermore, the history acquisition unit can analyze the user's social media activity history and suggest the optimal route. In this way, the history acquisition unit can provide optimal map information corresponding to social media activity. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's social media activity data into AI, and the AI ​​can generate the optimal route corresponding to the activity.

[0046] The generation unit can generate the optimal route when generating a map, taking into account the user's current destination. For example, when the user enters a destination, the generation unit generates the shortest route. The generation unit can also generate an efficient route when the user enters multiple destinations. Furthermore, the generation unit can generate a new route in real time when the user changes their destination. In this way, the generation unit can provide the optimal route according to the user's destination. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's destination data into AI, and the AI ​​can generate the optimal route according to the destination.

[0047] The generation unit can generate an optimal map by considering the user's past travel patterns during map generation. For example, the generation unit can generate an optimal map based on routes previously used by the user. It can also generate routes that avoid congestion based on the user's past travel patterns. Furthermore, the generation unit can analyze the user's past travel patterns and generate the most efficient map. This allows the generation unit to provide an optimal map based on past travel patterns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user travel pattern data into AI, which can then generate an optimal map based on past travel patterns.

[0048] The generation unit can generate an optimal map by considering the user's current activities during map generation. For example, if the user is at work, the generation unit can generate an efficient route. It can also generate a map highlighting tourist spots if the user is traveling. Furthermore, if the user is shopping, the generation unit can generate a map highlighting shopping areas. This allows the generation unit to provide an optimal map tailored to the user's current activities. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user activity data into an AI, which can then generate an optimal map based on the user's current activities.

[0049] The generation unit can generate an optimal map by considering the user's current time of day when generating a map. For example, during the morning commute, the generation unit can generate the shortest route. It can also generate a map highlighting lunch spots during lunchtime. Furthermore, it can generate a safe route during the evening. This allows the generation unit to provide an optimal map tailored to the user's current time of day. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's time data into an AI, which can then generate an optimal map based on the current time of day.

[0050] The service provider can select the optimal display method when providing maps, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a personal computer, the service provider can provide a display method that includes detailed information. This allows the service provider to provide the optimal display method according to the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into the AI, which can then select the optimal display method according to the device.

[0051] The service provider can select the optimal display method when providing maps, taking into account the user's current network status. For example, if the user is using Wi-Fi, the service provider can provide a high-resolution map. Furthermore, if the user is using 4G, the service provider can provide a map with reduced data usage. Additionally, if the user is using 5G, the service provider can provide a map that is updated in real time. This allows the service provider to provide the optimal display method according to the user's network status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's network status into AI, which can then select the optimal display method according to the network.

[0052] The service provider can adjust the map's color scheme according to the user's visual preferences when providing the map. For example, if the user prefers calm colors, the service provider can provide a simple and highly visible color scheme. Alternatively, if the user prefers bright colors, the service provider can provide a colorful and visually appealing color scheme. Furthermore, if the user prefers night mode, the service provider can provide a color scheme suitable for a dark background. In this way, the service provider can provide the optimal color scheme according to the user's visual preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's color scheme preferences into the AI, which can then select the optimal color scheme.

[0053] The service provider can provide map information in response to the user's voice instructions when providing a map. For example, if the user specifies a destination by voice, the service provider can display the route to that destination on the map. The service provider can also display information about a specific facility on the map if the user specifies a specific facility by voice. Furthermore, if the user confirms their current location by voice, the service provider can display the current location on the map. In this way, the service provider can provide optimal map information in response to the user's voice instructions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's voice instructions into AI, and the AI ​​can generate optimal map information in response to the voice instructions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The acquisition unit can acquire weather information for the current location and provide map information appropriate to the weather. For example, in rainy weather, the acquisition unit can prioritize displaying routes where umbrellas can be used or routes with roofs. In sunny weather, the acquisition unit can also prioritize displaying routes with good scenery or routes that pass through parks. Furthermore, on snowy days, the acquisition unit can prioritize displaying routes that are less slippery or routes that have been cleared of snow. In this way, the acquisition unit can provide optimal map information appropriate to the weather. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input weather data into AI, and the AI ​​can generate the optimal route appropriate to the weather.

[0056] The acquisition unit can acquire real-time traffic conditions at the current location and provide map information according to the degree of congestion. For example, if traffic congestion occurs, the acquisition unit can suggest an alternative route. The acquisition unit can also suggest an alternative route based on delay information for public transportation. Furthermore, if a traffic accident occurs, the acquisition unit can display that information on the map and warn the user. In this way, the acquisition unit can provide optimal map information according to the traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input traffic data into AI, and the AI ​​can generate an optimal route according to the degree of congestion.

[0057] The acquisition unit can acquire event information for the current location and provide map information corresponding to the event. For example, the acquisition unit can acquire information on events being held at the current location and display a route to the event venue. The acquisition unit can also acquire information on festivals and markets being held at the current location and display related facilities. Furthermore, the acquisition unit can acquire information on sporting events being held at the current location and display a route to the viewing location. In this way, the acquisition unit can provide optimal map information according to the event information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input event information into AI, and the AI ​​can generate an optimal route according to the event.

[0058] The acquisition unit can acquire safety information (such as crime rates) for the current location and provide map information tailored to the level of safety. For example, the acquisition unit can suggest a route that avoids areas with high crime rates. It can also highlight safe areas and suggest routes that allow for safe travel. Furthermore, the acquisition unit can display evacuation locations on the map in case of emergency and suggest evacuation routes. In this way, the acquisition unit can provide optimal map information tailored to the safety information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input safety information into AI, which can then generate an optimal route tailored to the level of safety.

[0059] The history acquisition unit can acquire the user's past modes of transportation and provide map information corresponding to those modes. For example, the history acquisition unit can suggest a route suitable for walking based on routes the user has traveled on foot in the past. It can also suggest a route suitable for cycling based on routes the user has traveled by bicycle in the past. Furthermore, it can suggest a route suitable for driving based on routes the user has traveled by car in the past. In this way, the history acquisition unit can provide optimal map information according to the mode of transportation. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's mode of transportation data into AI, and the AI ​​can generate an optimal route according to the mode of transportation.

[0060] The history acquisition unit can acquire the user's past visit frequency and provide map information according to that frequency. For example, the history acquisition unit can highlight places the user has frequently visited in the past and suggest routes. The history acquisition unit can also suggest related places based on places the user has visited only once in the past. Furthermore, the history acquisition unit can suggest the optimal route based on the visit frequency of places the user has visited in the past. In this way, the history acquisition unit can provide optimal map information according to the visit frequency. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's visit frequency data into AI, and the AI ​​can generate the optimal route according to the frequency.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The acquisition unit obtains information about the user's current location. For example, the acquisition unit obtains information such as whether the location is a city, a regional city, or a tourist destination. The acquisition unit can also obtain information such as congestion, normal, or quiet. Furthermore, the acquisition unit can obtain unique information about the location. For example, the acquisition unit can obtain information about the population density and major facilities in cities. The acquisition unit can also obtain information about tourist spots and restaurants in tourist destinations. Step 2: The history acquisition unit acquires the user's past activity history. For example, the history acquisition unit acquires the range of activity by acquiring latitude and longitude. The history acquisition unit can also acquire search history from smartphones and personal computers. Furthermore, the history acquisition unit can acquire past map usage status. For example, the history acquisition unit acquires places the user has visited in the past and the means of transportation. Step 3: The generation unit generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit. For example, the generation unit optimizes the map type and scale. It can also optimize the content and amount of information displayed for facilities. Furthermore, the generation unit can optimize accompanying important information. For example, in congested urban areas, the generation unit highlights major facilities and transportation information. In tourist areas, the generation unit can also display detailed information on tourist attractions and restaurants. Step 4: The provider unit provides the map generated by the generator unit. The provider unit, for example, displays the map on the user's device. The provider unit can also provide real-time map updates. Furthermore, the provider unit can provide map information tailored to the user's needs. For example, the provider unit can provide the optimal route to a delivery company. The provider unit can also provide travelers with the latest information on tourist destinations.

[0063] (Example of form 2) The map optimization system according to an embodiment of the present invention is a system that eliminates the need for users to manually perform various map settings and register information. The map optimization system improves convenience by having AI make real-time decisions based on the user's current location and past activity history, and providing this information to the user. For example, the map optimization system acquires the user's current location. For example, it acquires information such as whether it is a city, regional city, or tourist destination, and whether it is crowded, normal, or quiet. The map optimization system also acquires unique information about the area. Next, the map optimization system acquires past activity history. For example, it acquires the range of activity obtained by acquiring latitude and longitude, search history from smartphones and personal computers, and past map usage status. Based on this information, the map optimization system generates an optimal map in real time. Specifically, it optimizes the type and scale of the map, the content and amount of information displayed for facilities, and important accompanying information. For example, in a crowded urban area, major facilities and transportation information are highlighted, and in a tourist destination, information on tourist spots and restaurants is displayed in detail. This mechanism eliminates the need for users to manually set up maps and register information. Furthermore, the map optimization system can provide map information that reflects the latest conditions through real-time information updates. This improves user convenience and allows users to reach their destinations more efficiently. In addition, the map optimization system caters to a wide range of target audiences, including general users who use map apps daily, professionals such as delivery drivers and taxi drivers, and travelers and tourists. For example, delivery drivers can significantly improve their operational efficiency by being automatically provided with the optimal route. Travelers can also smoothly plan their trips by obtaining the latest information on tourist destinations in real time. Thus, the map optimization system not only improves user convenience but also contributes to improved operational efficiency, real-time response, and the realization of a sustainable society. For example, by providing efficient routes, it contributes to reducing fuel consumption and CO2 emissions, aiming for the realization of a sustainable society. In this way, the map optimization system can provide the optimal map in real time based on the user's current location and past activity history.

[0064] The map optimization system according to this embodiment comprises an acquisition unit, a history acquisition unit, a generation unit, and a provision unit. The acquisition unit acquires the user's current location information. The acquisition unit acquires information such as urban areas, regional cities, and tourist destinations. The acquisition unit can also acquire information such as congestion, normal conditions, and quiet areas. Furthermore, the acquisition unit can acquire unique information about the area. For example, the acquisition unit acquires information on urban population density and major facilities. The acquisition unit can also acquire information on tourist spots and restaurants in tourist destinations. The history acquisition unit acquires the user's past activity history. For example, the history acquisition unit acquires the range of activity by acquiring latitude and longitude. The history acquisition unit can also acquire search history from smartphones and personal computers. Furthermore, the history acquisition unit can acquire past map usage information. For example, the history acquisition unit acquires places the user has visited in the past and means of transportation. The generation unit generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit. The generation unit optimizes the map type and scale, for example. The generation unit can also optimize the display content and amount of facilities. Furthermore, the generation unit can optimize associated important information. For example, in congested urban areas, the generation unit can highlight key facilities and transportation information. In tourist areas, the generation unit can also display detailed information on tourist attractions and restaurants. The provisioning unit provides the map generated by the generation unit. The provisioning unit, for example, displays the map on the user's device. The provisioning unit can also provide map update information in real time. Furthermore, the provisioning unit can provide map information tailored to the user's needs. For example, the provisioning unit can provide the optimal route to a delivery company. The provisioning unit can also provide travelers with the latest information on tourist destinations. As a result, the map optimization system according to this embodiment can provide the optimal map in real time based on the user's current location and past activity history.

[0065] The data acquisition unit obtains information about the user's current location. Specifically, it collects GPS data from the user's device to determine their current location. Furthermore, the unit acquires information such as whether the location is a city, a regional city, or a tourist destination. For example, in cities, it acquires information on population density and major facilities, while in regional cities, it collects information on local specialties and events. In tourist destinations, it can acquire information on tourist spots and restaurants. This allows the data acquisition unit to understand detailed information about the user's current location. The data acquisition unit can also acquire information on conditions such as congestion, normal, and quiet. For example, it can analyze real-time pedestrian flow data to understand the congestion level in a specific area. This can provide useful information for users who want to avoid crowds or are looking for quiet places. Furthermore, the data acquisition unit can acquire unique information about a particular location. For example, it can acquire information on population density and major facilities in cities. It can also acquire information on tourist spots and restaurants in tourist destinations. This allows the unit to provide detailed information about places the user visits, supporting a more fulfilling experience. The data acquisition unit centrally manages this information and can collaborate with other systems and departments as needed. For example, the acquired data is stored on a cloud server, making it accessible to the history acquisition and generation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The history acquisition unit retrieves the user's past activity history. Specifically, it collects latitude and longitude data from the user's device to identify the user's past range of activity. For example, it can obtain information on places the user has visited and the means of transportation they have used in the past. This allows the system to understand the user's behavior patterns and provide map information tailored to their individual needs. The history acquisition unit can also acquire search history from smartphones and personal computers. For example, it can collect information on tourist spots and restaurants the user has searched for in the past and generate an optimal map based on this information. Furthermore, the history acquisition unit can acquire information on past map usage. For example, it can analyze the types of maps the user has used and the content displayed in the past to provide a map tailored to the user's preferences. This allows the history acquisition unit to gain a detailed understanding of the user's past activity history and provide optimal map information tailored to their individual needs. In addition, the history acquisition unit can centrally manage this data and collaborate with other systems and departments as needed. For example, data collected by the history acquisition unit can be stored on a cloud server and made accessible to the generation unit. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the history acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The generation unit generates the optimal map based on the information acquired by the acquisition unit and the history acquisition unit. Specifically, the generation unit considers the user's current location and past activity history to optimize the map type and scale. For example, in congested urban areas, it can highlight major facilities and transportation information, and in tourist areas, it can display detailed information on tourist spots and restaurants. The generation unit can also optimize the content and amount of information displayed for facilities. For example, it can prioritize displaying facilities and spots of interest to the user and omit unnecessary information to provide a map that is easy to view and use. Furthermore, the generation unit can optimize accompanying important information. For example, it can display current weather information, traffic conditions, and event information in real time to quickly provide the information the user needs. The generation unit centrally manages this information and can cooperate with other systems and departments as needed. For example, the generated maps are stored on a cloud server and made accessible to the provisioning unit. In addition, by adjusting the frequency and accuracy of map generation, flexible responses to specific situations and conditions are possible. As a result, the generation unit can generate maps efficiently and effectively, improving the overall performance of the system.

[0068] The service provider provides maps generated by the generation unit. Specifically, the service provider displays maps on the user's device. For example, it displays maps optimized for devices such as smartphones, tablets, and personal computers, making them easily accessible to users. The service provider can also provide map updates in real time. For example, it can update current traffic conditions, weather information, and event information in real time, ensuring users always have access to the latest information. Furthermore, the service provider can provide map information tailored to user needs. For example, it can provide optimal routes for delivery companies and the latest information on tourist destinations for travelers. This allows the service provider to quickly and accurately provide map information that meets the diverse needs of users. In addition, the service provider can collect user feedback and continuously improve the accuracy and content of the maps. For example, it can improve the accuracy of map display and route guidance based on user feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide map information to users, improving user convenience.

[0069] The data acquisition unit can acquire information on cities, regional cities, tourist destinations, etc. For example, the data acquisition unit can acquire information on the population density and major facilities of cities. It can also acquire information on traffic conditions and public facilities in regional cities. Furthermore, the data acquisition unit can acquire information on tourist spots and restaurants in tourist destinations. For example, the data acquisition unit can collect information on major transportation and commercial facilities in cities. It can also collect information on public transportation and major tourist spots in regional cities. Furthermore, the data acquisition unit can collect information on popular tourist spots and restaurants in tourist destinations. As a result, the data acquisition unit can acquire information tailored to the user's current location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input urban information into AI, and the AI ​​can analyze and acquire the information.

[0070] The history acquisition unit can acquire the user's range of activity by obtaining latitude and longitude coordinates. For example, the history acquisition unit can acquire the user's range of activity using GPS data. The history acquisition unit can also acquire the user's range of activity using location information services. Furthermore, the history acquisition unit can acquire the user's means of transportation and duration of stay. For example, the history acquisition unit collects latitude and longitude data of places the user has visited in the past. The history acquisition unit can also collect the means of transportation the user has used in the past and duration of stay. Furthermore, the history acquisition unit can analyze the user's behavior patterns and identify their range of activity. This allows the history acquisition unit to acquire the user's past range of activity. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input GPS data into AI, and the AI ​​can analyze the data to identify the range of activity.

[0071] The generation unit can optimize the type and scale of the map. For example, it can optimize the type of map, such as road maps or tourist maps. It can also optimize the scale, such as 1:1000 or 1:5000. Furthermore, the generation unit can adjust the type and scale of the map according to the user's needs. For example, in congested urban areas, the generation unit can highlight major facilities and transportation information. In tourist areas, the generation unit can display detailed information on tourist spots and restaurants. Furthermore, in regional cities, the generation unit can display information on public transportation and major tourist spots. In this way, the generation unit can generate the optimal map according to the user's needs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the type and scale of the map into the AI, and the AI ​​can generate the optimal map.

[0072] The information provider can optimize the content and amount of information displayed for each facility. For example, it can optimize the types of facilities to display. It can also optimize the level of detail of the information displayed. Furthermore, it can adjust the content and amount of information displayed for each facility according to the user's needs. For example, in congested urban areas, the information provider can highlight major facilities and transportation information. In tourist areas, it can also display detailed information on tourist spots and restaurants. Furthermore, in regional cities, it can display information on public transportation and major tourist spots. This allows the information provider to provide users with the information they need in the most optimal way. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the content and amount of information to be displayed for each facility into the AI, which can then determine the optimal display method.

[0073] The acquisition unit can estimate the user's emotions and adjust the frequency of acquiring current location information based on the estimated emotions. For example, if the user is stressed, the acquisition unit can set the frequency of acquiring current location information to a low level and limit the information updates. Conversely, if the user is relaxed, the acquisition unit can set the frequency of acquiring current location information to a high level and provide more detailed information. Furthermore, if the user is in a hurry, the acquisition unit can set the frequency of acquiring current location information to a moderate level and provide only the necessary information. In this way, the acquisition unit can provide more appropriate information by adjusting the frequency of acquiring current location information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust the frequency of acquiring the current location's status based on the result.

[0074] The acquisition unit can acquire weather information for the current location and provide map information appropriate to the weather. For example, in rainy weather, the acquisition unit can prioritize displaying routes where umbrellas can be used or routes with roofs. In sunny weather, the acquisition unit can also prioritize displaying routes with good scenery or routes that pass through parks. Furthermore, on snowy days, the acquisition unit can prioritize displaying routes that are less slippery or routes that have been cleared of snow. In this way, the acquisition unit can provide optimal map information appropriate to the weather. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input weather data into AI, and the AI ​​can generate the optimal route appropriate to the weather.

[0075] The acquisition unit can acquire real-time traffic conditions at the current location and provide map information according to the degree of congestion. For example, if traffic congestion occurs, the acquisition unit can suggest an alternative route. The acquisition unit can also suggest an alternative route based on delay information for public transportation. Furthermore, if a traffic accident occurs, the acquisition unit can display that information on the map and warn the user. In this way, the acquisition unit can provide optimal map information according to the traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input traffic data into AI, and the AI ​​can generate an optimal route according to the degree of congestion.

[0076] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring only important information. If the user is relaxed, the data acquisition unit can also prioritize acquiring detailed information. Furthermore, if the user is in a hurry, the data acquisition unit can prioritize acquiring only the minimum necessary information. In this way, the data acquisition unit can provide more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input the user's facial expression data into the generative AI, the generative AI can estimate emotions, and the data acquisition unit can determine the priority of information based on the result.

[0077] The acquisition unit can acquire event information for the current location and provide map information corresponding to the event. For example, the acquisition unit can acquire information on events being held at the current location and display a route to the event venue. The acquisition unit can also acquire information on festivals and markets being held at the current location and display related facilities. Furthermore, the acquisition unit can acquire information on sporting events being held at the current location and display a route to the viewing location. In this way, the acquisition unit can provide optimal map information according to the event information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input event information into AI, and the AI ​​can generate an optimal route according to the event.

[0078] The acquisition unit can acquire safety information (such as crime rates) for the current location and provide map information tailored to the level of safety. For example, the acquisition unit can suggest a route that avoids areas with high crime rates. It can also highlight safe areas and suggest routes that allow for safe travel. Furthermore, the acquisition unit can display evacuation locations on the map in case of emergency and suggest evacuation routes. In this way, the acquisition unit can provide optimal map information tailored to the safety information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input safety information into AI, which can then generate an optimal route tailored to the level of safety.

[0079] The history acquisition unit can estimate the user's emotions and adjust the frequency of acquiring behavioral history based on the estimated emotions. For example, if the user is stressed, the history acquisition unit can set the frequency of acquiring behavioral history to a low level and limit the information updates. Conversely, if the user is relaxed, the history acquisition unit can set the frequency of acquiring behavioral history to a high level and provide more detailed information. Furthermore, if the user is in a hurry, the history acquisition unit can set the frequency of acquiring behavioral history to a moderate level and provide only the necessary information. In this way, the history acquisition unit can provide more appropriate information by adjusting the frequency of acquiring behavioral history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without using AI. For example, the history acquisition unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust the frequency of acquiring behavioral history based on the result.

[0080] The history acquisition unit can acquire the user's past modes of transportation and provide map information corresponding to those modes. For example, the history acquisition unit can suggest a route suitable for walking based on routes the user has traveled on foot in the past. It can also suggest a route suitable for cycling based on routes the user has traveled by bicycle in the past. Furthermore, it can suggest a route suitable for driving based on routes the user has traveled by car in the past. In this way, the history acquisition unit can provide optimal map information according to the mode of transportation. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's mode of transportation data into AI, and the AI ​​can generate an optimal route according to the mode of transportation.

[0081] The history acquisition unit can acquire the user's past visit frequency and provide map information according to that frequency. For example, the history acquisition unit can highlight places the user has frequently visited in the past and suggest routes. The history acquisition unit can also suggest related places based on places the user has visited only once in the past. Furthermore, the history acquisition unit can suggest the optimal route based on the visit frequency of places the user has visited in the past. In this way, the history acquisition unit can provide optimal map information according to the visit frequency. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's visit frequency data into AI, and the AI ​​can generate the optimal route according to the frequency.

[0082] The history acquisition unit can estimate the user's emotions and determine the priority of the history to acquire based on the estimated user emotions. For example, if the user is stressed, the history acquisition unit will prioritize acquiring only important history. If the user is relaxed, the history acquisition unit can also prioritize acquiring detailed history. Furthermore, if the user is in a hurry, the history acquisition unit can prioritize acquiring only the minimum necessary history. In this way, the history acquisition unit can provide more appropriate information by determining the priority of history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history acquisition unit may be performed using AI, or not using AI. For example, the history acquisition unit can input the user's facial expression data into the generative AI, the generative AI can estimate emotions, and the history priority can be determined based on the result.

[0083] The history acquisition unit can acquire a user's past purchase history and provide map information corresponding to that purchase history. For example, the history acquisition unit can display stores related to products the user has previously purchased on a map. The history acquisition unit can also display relevant events and sales information on a map based on the user's purchase history. Furthermore, the history acquisition unit can analyze the user's purchase history and suggest the optimal shopping route. In this way, the history acquisition unit can provide optimal map information according to the purchase history. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's purchase history data into AI, and the AI ​​can generate the optimal route according to the purchase history.

[0084] The history acquisition unit can acquire a user's past social media activity and provide map information corresponding to that activity. For example, the history acquisition unit can display on a map the locations where the user has previously checked in. The history acquisition unit can also suggest relevant locations based on the content of the user's social media posts. Furthermore, the history acquisition unit can analyze the user's social media activity history and suggest the optimal route. In this way, the history acquisition unit can provide optimal map information corresponding to social media activity. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's social media activity data into AI, and the AI ​​can generate the optimal route corresponding to the activity.

[0085] The generation unit can estimate the user's emotions and adjust the map generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed map. If the user is in a hurry, the generation unit can also generate a concise map. Furthermore, if the user is excited, the generation unit can generate a visually stimulating map. In this way, the generation unit can provide a more appropriate map by adjusting the map generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI, the generation AI can estimate emotions, and the map generation method can be adjusted based on the result.

[0086] The generation unit can generate the optimal route when generating a map, taking into account the user's current destination. For example, when the user enters a destination, the generation unit generates the shortest route. The generation unit can also generate an efficient route when the user enters multiple destinations. Furthermore, the generation unit can generate a new route in real time when the user changes their destination. In this way, the generation unit can provide the optimal route according to the user's destination. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's destination data into AI, and the AI ​​can generate the optimal route according to the destination.

[0087] The generation unit can generate an optimal map by considering the user's past travel patterns during map generation. For example, the generation unit can generate an optimal map based on routes previously used by the user. It can also generate routes that avoid congestion based on the user's past travel patterns. Furthermore, the generation unit can analyze the user's past travel patterns and generate the most efficient map. This allows the generation unit to provide an optimal map based on past travel patterns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user travel pattern data into AI, which can then generate an optimal map based on past travel patterns.

[0088] The generation unit can estimate the user's emotions and adjust the map display content based on the estimated emotions. For example, if the user is relaxed, the generation unit can display detailed information. If the user is in a hurry, the generation unit can also display concise information. Furthermore, if the user is excited, the generation unit can display visually stimulating information. In this way, the generation unit can provide more appropriate information by adjusting the map display content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI, the generation AI can estimate emotions, and the map display content can be adjusted based on the result.

[0089] The generation unit can generate an optimal map by considering the user's current activities during map generation. For example, if the user is at work, the generation unit can generate an efficient route. It can also generate a map highlighting tourist spots if the user is traveling. Furthermore, if the user is shopping, the generation unit can generate a map highlighting shopping areas. This allows the generation unit to provide an optimal map tailored to the user's current activities. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user activity data into an AI, which can then generate an optimal map based on the user's current activities.

[0090] The generation unit can generate an optimal map by considering the user's current time of day when generating a map. For example, during the morning commute, the generation unit can generate the shortest route. It can also generate a map highlighting lunch spots during lunchtime. Furthermore, it can generate a safe route during the evening. This allows the generation unit to provide an optimal map tailored to the user's current time of day. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's time data into an AI, which can then generate an optimal map based on the current time of day.

[0091] The service provider can estimate the user's emotions and adjust the way the map is presented based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. In this way, the service provider can provide more appropriate information by adjusting the way the map is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the service provider can adjust the way the map is presented based on the result.

[0092] The service provider can select the optimal display method when providing maps, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a personal computer, the service provider can provide a display method that includes detailed information. This allows the service provider to provide the optimal display method according to the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into the AI, which can then select the optimal display method according to the device.

[0093] The service provider can select the optimal display method when providing maps, taking into account the user's current network status. For example, if the user is using Wi-Fi, the service provider can provide a high-resolution map. Furthermore, if the user is using 4G, the service provider can provide a map with reduced data usage. Additionally, if the user is using 5G, the service provider can provide a map that is updated in real time. This allows the service provider to provide the optimal display method according to the user's network status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's network status into AI, which can then select the optimal display method according to the network.

[0094] The service provider can estimate the user's emotions and adjust the frequency of map provision based on the estimated emotions. For example, if the user is stressed, the service provider can set the map provision frequency low and limit information updates. Conversely, if the user is relaxed, the service provider can set the map provision frequency high and provide more detailed information. Furthermore, if the user is in a hurry, the service provider can set the map provision frequency to a moderate level and provide only the necessary information. In this way, the service provider can provide more appropriate information by adjusting the map provision frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the map provision frequency can be adjusted based on the result.

[0095] The service provider can adjust the map's color scheme according to the user's visual preferences when providing the map. For example, if the user prefers calm colors, the service provider can provide a simple and highly visible color scheme. Alternatively, if the user prefers bright colors, the service provider can provide a colorful and visually appealing color scheme. Furthermore, if the user prefers night mode, the service provider can provide a color scheme suitable for a dark background. In this way, the service provider can provide the optimal color scheme according to the user's visual preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's color scheme preferences into the AI, which can then select the optimal color scheme.

[0096] The service provider can provide map information in response to the user's voice instructions when providing a map. For example, if the user specifies a destination by voice, the service provider can display the route to that destination on the map. The service provider can also display information about a specific facility on the map if the user specifies a specific facility by voice. Furthermore, if the user confirms their current location by voice, the service provider can display the current location on the map. In this way, the service provider can provide optimal map information in response to the user's voice instructions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's voice instructions into AI, and the AI ​​can generate optimal map information in response to the voice instructions.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The acquisition unit can estimate the user's emotions and adjust the frequency of acquiring current location information based on the estimated emotions. For example, if the user is stressed, the acquisition unit can set the frequency of acquiring current location information to a low level and limit the information updates. Conversely, if the user is relaxed, the acquisition unit can set the frequency of acquiring current location information to a high level and provide more detailed information. Furthermore, if the user is in a hurry, the acquisition unit can set the frequency of acquiring current location information to a moderate level and provide only the necessary information. In this way, the acquisition unit can provide more appropriate information by adjusting the frequency of acquiring current location information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust the frequency of acquiring the current location's status based on the result.

[0099] The acquisition unit can acquire weather information for the current location and provide map information appropriate to the weather. For example, in rainy weather, the acquisition unit can prioritize displaying routes where umbrellas can be used or routes with roofs. In sunny weather, the acquisition unit can also prioritize displaying routes with good scenery or routes that pass through parks. Furthermore, on snowy days, the acquisition unit can prioritize displaying routes that are less slippery or routes that have been cleared of snow. In this way, the acquisition unit can provide optimal map information appropriate to the weather. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input weather data into AI, and the AI ​​can generate the optimal route appropriate to the weather.

[0100] The acquisition unit can acquire real-time traffic conditions at the current location and provide map information according to the degree of congestion. For example, if traffic congestion occurs, the acquisition unit can suggest an alternative route. The acquisition unit can also suggest an alternative route based on delay information for public transportation. Furthermore, if a traffic accident occurs, the acquisition unit can display that information on the map and warn the user. In this way, the acquisition unit can provide optimal map information according to the traffic conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input traffic data into AI, and the AI ​​can generate an optimal route according to the degree of congestion.

[0101] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring only important information. If the user is relaxed, the data acquisition unit can also prioritize acquiring detailed information. Furthermore, if the user is in a hurry, the data acquisition unit can prioritize acquiring only the minimum necessary information. In this way, the data acquisition unit can provide more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input the user's facial expression data into the generative AI, the generative AI can estimate emotions, and the data acquisition unit can determine the priority of information based on the result.

[0102] The acquisition unit can acquire event information for the current location and provide map information corresponding to the event. For example, the acquisition unit can acquire information on events being held at the current location and display a route to the event venue. The acquisition unit can also acquire information on festivals and markets being held at the current location and display related facilities. Furthermore, the acquisition unit can acquire information on sporting events being held at the current location and display a route to the viewing location. In this way, the acquisition unit can provide optimal map information according to the event information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input event information into AI, and the AI ​​can generate an optimal route according to the event.

[0103] The acquisition unit can acquire safety information (such as crime rates) for the current location and provide map information tailored to the level of safety. For example, the acquisition unit can suggest a route that avoids areas with high crime rates. It can also highlight safe areas and suggest routes that allow for safe travel. Furthermore, the acquisition unit can display evacuation locations on the map in case of emergency and suggest evacuation routes. In this way, the acquisition unit can provide optimal map information tailored to the safety information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input safety information into AI, which can then generate an optimal route tailored to the level of safety.

[0104] The history acquisition unit can estimate the user's emotions and adjust the frequency of acquiring behavioral history based on the estimated emotions. For example, if the user is stressed, the history acquisition unit can set the frequency of acquiring behavioral history to a low level and limit the information updates. Conversely, if the user is relaxed, the history acquisition unit can set the frequency of acquiring behavioral history to a high level and provide more detailed information. Furthermore, if the user is in a hurry, the history acquisition unit can set the frequency of acquiring behavioral history to a moderate level and provide only the necessary information. In this way, the history acquisition unit can provide more appropriate information by adjusting the frequency of acquiring behavioral history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without using AI. For example, the history acquisition unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust the frequency of acquiring behavioral history based on the result.

[0105] The history acquisition unit can acquire the user's past modes of transportation and provide map information corresponding to those modes. For example, the history acquisition unit can suggest a route suitable for walking based on routes the user has traveled on foot in the past. It can also suggest a route suitable for cycling based on routes the user has traveled by bicycle in the past. Furthermore, it can suggest a route suitable for driving based on routes the user has traveled by car in the past. In this way, the history acquisition unit can provide optimal map information according to the mode of transportation. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's mode of transportation data into AI, and the AI ​​can generate an optimal route according to the mode of transportation.

[0106] The history acquisition unit can acquire the user's past visit frequency and provide map information according to that frequency. For example, the history acquisition unit can highlight places the user has frequently visited in the past and suggest routes. The history acquisition unit can also suggest related places based on places the user has visited only once in the past. Furthermore, the history acquisition unit can suggest the optimal route based on the visit frequency of places the user has visited in the past. In this way, the history acquisition unit can provide optimal map information according to the visit frequency. Some or all of the above processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input the user's visit frequency data into AI, and the AI ​​can generate the optimal route according to the frequency.

[0107] The history acquisition unit can estimate the user's emotions and determine the priority of the history to acquire based on the estimated user emotions. For example, if the user is stressed, the history acquisition unit will prioritize acquiring only important history. If the user is relaxed, the history acquisition unit can also prioritize acquiring detailed history. Furthermore, if the user is in a hurry, the history acquisition unit can prioritize acquiring only the minimum necessary history. In this way, the history acquisition unit can provide more appropriate information by determining the priority of history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history acquisition unit may be performed using AI, or not using AI. For example, the history acquisition unit can input the user's facial expression data into the generative AI, the generative AI can estimate emotions, and the history priority can be determined based on the result.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The acquisition unit obtains information about the user's current location. For example, the acquisition unit obtains information such as whether the location is a city, a regional city, or a tourist destination. The acquisition unit can also obtain information such as congestion, normal, or quiet. Furthermore, the acquisition unit can obtain unique information about the location. For example, the acquisition unit can obtain information about the population density and major facilities in cities. The acquisition unit can also obtain information about tourist spots and restaurants in tourist destinations. Step 2: The history acquisition unit acquires the user's past activity history. For example, the history acquisition unit acquires the range of activity by acquiring latitude and longitude. The history acquisition unit can also acquire search history from smartphones and personal computers. Furthermore, the history acquisition unit can acquire past map usage status. For example, the history acquisition unit acquires places the user has visited in the past and the means of transportation. Step 3: The generation unit generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit. For example, the generation unit optimizes the map type and scale. It can also optimize the content and amount of information displayed for facilities. Furthermore, the generation unit can optimize accompanying important information. For example, in congested urban areas, the generation unit highlights major facilities and transportation information. In tourist areas, the generation unit can also display detailed information on tourist attractions and restaurants. Step 4: The provider unit provides the map generated by the generator unit. The provider unit, for example, displays the map on the user's device. The provider unit can also provide real-time map updates. Furthermore, the provider unit can provide map information tailored to the user's needs. For example, the provider unit can provide the optimal route to a delivery company. The provider unit can also provide travelers with the latest information on tourist destinations.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the acquisition unit, history acquisition unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the user's current location using the camera 42 and communication I / F 44 of the smart device 14. The history acquisition unit acquires the user's past activity history using the specific processing unit 290 of the data processing unit 12. The generation unit generates an optimal map using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the map generated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the acquisition unit, history acquisition unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the user's current location using the camera 42 and communication I / F 44 of the smart glasses 214. The history acquisition unit acquires the user's past activity history using the identification processing unit 290 of the data processing unit 12. The generation unit generates an optimal map using the identification processing unit 290 of the data processing unit 12. The provision unit provides the user with the map generated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the acquisition unit, history acquisition unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the user's current location using the camera 42 and communication I / F 44 of the headset terminal 314. The history acquisition unit acquires the user's past activity history using the specific processing unit 290 of the data processing unit 12. The generation unit generates an optimal map using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the map generated by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the acquisition unit, history acquisition unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the user's current location using the camera 42 and communication I / F 44 of the robot 414. The history acquisition unit acquires the user's past activity history using the specific processing unit 290 of the data processing unit 12. The generation unit generates an optimal map using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the map generated by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A unit that acquires the current location status, A history acquisition unit that acquires past behavioral history, A generation unit that generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit, The system comprises a providing unit that provides the map generated by the generation unit. A system characterized by the following features. (Note 2) The acquisition unit is, Obtain information on cities, regional cities, tourist destinations, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The history acquisition unit, Obtaining latitude and longitude to determine the range of movement The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Optimize map type and scale The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Optimize the content and amount of information displayed for facilities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of location status updates based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It obtains weather information for the current location and provides map information corresponding to the weather. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, It obtains real-time traffic conditions at your current location and provides map information tailored to the level of congestion. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It retrieves event information for the current location and provides map information corresponding to the event. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, It obtains safety information for the current location and provides map information according to the safety level. The system described in Appendix 1, characterized by the features described herein. (Note 12) The history acquisition unit, The system estimates the user's emotions and adjusts the frequency of collecting behavioral history based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The history acquisition unit, The system retrieves the user's past modes of transportation and provides map information corresponding to those modes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The history acquisition unit, The system retrieves the user's past visit frequency and provides map information corresponding to that frequency. The system described in Appendix 1, characterized by the features described herein. (Note 15) The history acquisition unit, It estimates the user's emotions and determines the priority of the history to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The history acquisition unit, The system retrieves the user's past purchase history and provides map information tailored to that history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The history acquisition unit, It retrieves users' past social media activity and provides map information corresponding to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is We estimate the user's emotions and adjust the map generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a map, the system considers the user's current destination to generate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a map, the system considers the user's past travel patterns to create the optimal map. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the user's emotions and adjusts the map display based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a map, the system takes the user's current activity into consideration to create the optimal map. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a map, the system takes into account the user's current time zone to create the optimal map. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate the user's emotions and adjust how the map is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing maps, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing maps, the optimal display method is selected considering the user's current network status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates user sentiment and adjusts the frequency of map provision based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing maps, the map's color scheme is adjusted according to the user's visual preferences. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing maps, map information is provided in response to the user's voice commands. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit that acquires the current location status, A history acquisition unit that acquires past behavioral history, A generation unit that generates an optimal map based on the information acquired by the acquisition unit and the history acquisition unit, The system comprises a providing unit that provides the map generated by the generation unit. A system characterized by the following features.

2. The acquisition unit is, Obtain information on cities, regional cities, tourist destinations, etc. The system according to feature 1.

3. The history acquisition unit, Obtaining latitude and longitude to determine the range of movement The system according to feature 1.

4. The generating unit is Optimize map type and scale The system according to feature 1.

5. The aforementioned supply unit is, Optimize the content and amount of information displayed for facilities. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of location status updates based on the estimated emotions. The system according to feature 1.

7. The acquisition unit is, It obtains weather information for the current location and provides map information corresponding to the weather. The system according to feature 1.

8. The acquisition unit is, It obtains real-time traffic conditions at your current location and provides map information tailored to the level of congestion. The system according to feature 1.

9. The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system according to feature 1.

10. The acquisition unit is, It retrieves event information for the current location and provides map information corresponding to the event. The system according to feature 1.

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