system

Abstract

The system according to this embodiment aims to enable even users with a poor sense of direction to reach their destination without getting lost. [Solution] The system according to the embodiment comprises a reception unit, a calculation unit, a generation unit, and a display unit. The reception unit receives input of a destination. The calculation unit calculates a route based on the information received by the reception unit. The generation unit generates rays along the route calculated by the calculation unit. The display unit displays the rays 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user with direction dyslexia to reach the destination without getting lost.

[0005] The system according to the embodiment aims to enable a user with direction dyslexia to reach the destination without getting lost.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a calculation unit, a generation unit, and a display unit. The reception unit receives an input of a destination. The calculation unit calculates a route based on the information received by the reception unit. The generation unit generates a light beam along the route calculated by the calculation unit. The display unit displays the light beam generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable even users with a poor sense of direction to reach their destination without getting lost. [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 navigation system according to an embodiment of the present invention is a system for people with a poor sense of direction to reach their destination without getting lost. This navigation system operates when the user launches an application built into their mobile device and inputs a destination. The application analyzes the input information and calculates the route from the current location to the destination. The application calculates the optimal route based on map data and generates a ray of light along that route. The generated ray of light appears along the ground from the mobile device. The user can reach their destination simply by following this ray of light. This allows even people with a poor sense of direction to reach their destination without getting lost. For example, the user launches an application built into their mobile device and inputs a destination. For example, they input "I want to go to the station." This information is input into the application. Next, the application analyzes the input information and calculates the route from the current location to the destination. The application calculates the optimal route based on map data and generates a ray of light along that route. For example, if the user inputs a route to the station, a ray of light along that route is generated. The generated ray of light appears along the ground from the mobile device. For example, if the user is walking with their mobile device, the ray of light appears along the ground, and the user can reach their destination simply by following the ray of light. This allows even those with a poor sense of direction to reach their destination without getting lost. The system enables users to receive intuitive navigation without complex operations. Furthermore, the beam of light appears along the ground, allowing users to walk safely. For example, if a user is walking with a mobile device, the beam of light appears along the ground, allowing the user to walk safely. This ensures that even those with a poor sense of direction can reach their destination without getting lost.

[0029] The navigation system according to the embodiment comprises a reception unit, a calculation unit, a generation unit, and a display unit. The reception unit provides an interface for the user to input a destination. The reception unit can accept the destination by methods such as text input, voice input, or touch input. For example, if the user inputs "I want to go to the station," the reception unit accepts that information. The calculation unit calculates the route from the current location to the destination based on the information accepted by the reception unit. The calculation unit calculates the optimal route based on map data. For example, the calculation unit can calculate the shortest distance, the shortest time, or a route that takes traffic conditions into consideration. For example, if the user inputs a route to the station, the calculation unit calculates that route. The generation unit generates rays of light along the route calculated by the calculation unit. The generation unit can generate rays of light by methods such as lasers, LEDs, or projection. For example, if the user inputs a route to the station, the generation unit generates rays of light along that route. The display unit displays the rays of light generated by the generation unit. The display unit can display the rays of light on the screen of a mobile device, for example. The display unit, for example, displays a beam of light appearing along the ground when the user is walking with a mobile device. This allows the navigation system according to the embodiment to reach its destination without getting lost. The elements of the reception unit, calculation unit, generation unit, and display unit operate sequentially in relation to each other. For example, the reception unit receives the destination, the calculation unit calculates the route, the generation unit generates the beam of light, and the display unit displays the beam of light. This allows the user to reach their destination simply by following the beam of light.

[0030] The reception desk provides an interface for users to input their destinations. The reception desk allows users to input destinations using methods such as text input, voice input, and touch input. Specifically, with text input, users can use a keyboard to enter the name or address of their destination. With voice input, users speak their destination into a microphone, and speech recognition technology converts what they say into text. With touch input, users can intuitively specify their destination by touching it on the map. These input methods offer multiple options to enhance user convenience, allowing users to choose the method that is easiest for them. Furthermore, the reception desk also has the ability to accurately analyze the entered destination information and complete or correct it as needed. For example, if a user enters an ambiguous place name, the reception desk will suggest alternative locations to help the user select the correct destination. In the case of voice input, filtering and noise cancellation technologies are used to improve the accuracy of speech recognition, taking into account background noise and pronunciation differences. This allows the reception desk to enable users to input destinations easily and accurately, improving the usability of the navigation system.

[0031] The calculation unit calculates the route from the user's current location to their destination based on the information received by the reception unit. The calculation unit calculates the optimal route based on map data. Specifically, the calculation unit can calculate the shortest distance, the shortest time, and routes that take traffic conditions into consideration. For example, when calculating the shortest distance route, the calculation unit identifies the route that allows the user to reach their destination in the shortest distance based on the lengths of roads and sidewalks on the map data. When calculating the shortest time route, the calculation unit identifies the route that allows the user to reach their destination in the shortest time by considering traffic conditions and waiting times at traffic lights. When calculating a route that takes traffic conditions into consideration, the calculation unit obtains real-time traffic information and identifies a route that avoids the effects of congestion and accidents. In this way, the calculation unit can provide the optimal route that meets the user's needs. Furthermore, the calculation unit can learn the user's past travel history and preferences and provide personalized route suggestions. For example, if a user has frequently used a particular route in the past, the calculation unit will prioritize suggesting that route. The calculation unit can also calculate routes according to the user's mode of transportation (walking, cycling, driving, etc.). This allows the calculation unit to provide flexible route suggestions tailored to the user's travel style and preferences, thereby improving the convenience of the navigation system.

[0032] The generation unit generates a ray of light along the route calculated by the calculation unit. The generation unit can generate the ray using methods such as lasers, LEDs, and projection. Specifically, when using a laser, the generation unit uses high-precision laser light to project a ray indicating the route onto the ground or a wall. When using LEDs, the generation unit combines multiple LEDs to form a line of light along the route. When using projection, the generation unit uses a projector to project navigation information such as maps and arrows onto the ground or a wall. This allows the user to visually confirm the route as they move. Furthermore, the generation unit also has a function to adjust the color and brightness of the ray. For example, in bright daytime environments, the brightness of the ray is increased, and in nighttime or dark environments, the color of the ray is changed to improve visibility. In addition, the generation unit can adjust the display position and direction of the ray in real time according to the user's movement speed and direction. This allows the user to always confirm the route with optimal visibility. Furthermore, the generation unit has a function to generate individual rays to accommodate simultaneous use by multiple users. This allows the generation unit to provide visually easy-to-understand navigation information so that users can reach their destination without getting lost.

[0033] The display unit displays the light rays generated by the generation unit. For example, the display unit can display the light rays on the screen of a mobile device. Specifically, it uses augmented reality (AR) technology, which overlays the light rays onto the real-world scenery using the mobile device's camera. This allows the user to navigate while viewing the light rays aligned with the real-world scenery through the mobile device's screen. For example, if a user is walking with a mobile device, the light rays will appear to appear along the ground, allowing the user to reach their destination simply by following them. Furthermore, the display unit also has a function to customize how the light rays are displayed. For example, if a user has color blindness, the color of the light rays can be changed to improve visibility. The display unit can also change the thickness and shape of the light rays according to the user's preference. This allows the display unit to provide flexible displays tailored to the individual needs of the user. Additionally, the display unit can provide navigation information through non-visual means by combining it with additional functions such as voice guidance and vibration notifications. For example, if a user loses sight of the light rays, voice guidance can direct them to the next direction, or vibration notifications can alert them. This allows the display unit to provide visual and auditory navigation information so that users can reach their destination without getting lost.

[0034] The generation unit includes an adjustment unit that adjusts the intensity and color of the light rays. The adjustment unit can, for example, adjust the intensity of the light rays in units such as lumens or candelas. The adjustment unit can, for example, adjust the color of the light rays based on criteria such as RGB values ​​or color temperature. The adjustment unit can, for example, weaken the intensity of the light rays if the user is in a bright place. Also, the adjustment unit can, for example, strengthen the intensity of the light rays if the user is in a dark place. Furthermore, the adjustment unit can, for example, adjust the color of the light rays according to the user's preference. In this way, by adjusting the intensity and color of the light rays, it is possible to provide light rays that are easy for the user to see. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user environment information into the AI ​​and have the AI ​​perform the adjustment of the intensity and color of the light rays.

[0035] The generation unit includes a safety unit to ensure the safety of the light rays. The safety unit may include, for example, a filter to confirm that the light rays are safe for the eyes. The safety unit may also include, for example, a sensor to confirm that the light rays are safe for the skin. The safety unit can ensure safety for the eyes and skin by, for example, adjusting the intensity of the light rays. This ensures that the user can reach their destination safely by ensuring the safety of the light rays. Some or all of the above-described processes in the safety unit may be performed using, for example, AI, or not using AI. For example, the safety unit can input the intensity and color adjustment of the light rays to the AI ​​and have the AI ​​perform the safety check.

[0036] The display unit displays light rays along the ground. The display unit can display light rays along the ground using, for example, projection technology. The display unit can achieve ground-following display by, for example, adjusting the display angle and range of the light rays. This allows users to intuitively follow a route by displaying light rays along the ground. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input adjustments to the projection angle and range to the AI ​​and have the AI ​​perform the ground-following display.

[0037] The calculation unit calculates the optimal route based on map data. The calculation unit can calculate, for example, the shortest distance, the shortest time, and a route that takes traffic conditions into account. The calculation unit can optimize the route based on, for example, real-time traffic information. The calculation unit can calculate, for example, the optimal route from the user's current location to the destination. This allows the user to reach their destination efficiently by calculating the optimal route based on map data. Some or all of the above processing in the calculation unit may be performed using, for example, AI, or not using AI. For example, the calculation unit can input map data and traffic information into AI and have the AI ​​perform the calculation of the optimal route.

[0038] The reception desk analyzes the user's past destination input history and automatically suggests frequently entered destinations. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. This reduces the effort required for input by suggesting destinations based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest frequently entered destinations.

[0039] The reception desk suggests the most suitable destination based on the user's current activity status and schedule when the user enters a destination. For example, the reception desk can refer to the user's calendar information and suggest a destination based on their schedule. For example, the reception desk can suggest an appropriate destination based on the user's current activity status (e.g., exercising or resting). For example, the reception desk can analyze the user's past behavior patterns and suggest the most suitable destination for the current situation. This improves convenience by suggesting destinations that match the user's activity status and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into the AI ​​and have the AI ​​suggest the most suitable destination.

[0040] The reception unit, upon receiving destination input, prioritizes displaying highly relevant destinations by considering the user's geographical location. For example, the reception unit can prioritize displaying destinations close to the user's current location. For example, the reception unit can suggest easily accessible destinations by considering the user's means of transportation from their current location. For example, the reception unit can prioritize displaying popular spots around the user's current location. In this way, highly relevant destinations can be displayed by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​suggest highly relevant destinations.

[0041] The reception desk analyzes the user's social media activity when a destination is entered and suggests relevant destinations. For example, the reception desk can suggest relevant destinations based on places the user has checked in to on social media. For example, the reception desk can suggest relevant destinations based on places the user's friends have visited. For example, the reception desk can suggest relevant destinations based on events or places the user has shown interest in on social media. This allows the system to display destinations that match the user's interests by suggesting destinations based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​suggest relevant destinations.

[0042] The calculation unit predicts the optimal route by referring to past route data during route calculation. For example, the calculation unit can suggest the optimal route based on routes previously used by the user. For example, the calculation unit can suggest a route that avoids congestion based on the user's past travel history. For example, the calculation unit can analyze the user's past travel history and suggest the most efficient route. In this way, by predicting the optimal route based on past route data, an efficient route can be provided. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past route data into AI and have the AI ​​perform the prediction of the optimal route.

[0043] The calculation unit optimizes routes by considering traffic conditions and weather information during route calculation. For example, the calculation unit can propose the optimal route based on real-time traffic congestion information. For example, the calculation unit can propose the optimal route by considering the real-time operating status of public transportation. For example, the calculation unit can propose the optimal route based on real-time weather information. In this way, the optimal route can be provided by considering traffic conditions and weather information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input traffic conditions and weather information into AI and have the AI ​​perform the calculation of the optimal route.

[0044] The calculation unit proposes the optimal route by considering the user's geographical location information during route calculation. For example, the calculation unit can propose the route closest to the user's current location. For example, the calculation unit can propose the optimal route by considering the means of transportation from the user's current location. For example, the calculation unit can propose the optimal route by considering the traffic conditions around the user's current location. In this way, the optimal route can be provided by considering geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information into AI and have AI perform the calculation of the optimal route.

[0045] The calculation unit analyzes the user's social media activity and proposes relevant routes when calculating routes. For example, the calculation unit can propose relevant routes based on places the user has checked in to on social media. For example, the calculation unit can propose relevant routes based on places the user's friends have visited. For example, the calculation unit can propose relevant routes based on events or places the user has shown interest in on social media. In this way, by proposing routes based on social media activity, routes that match the user's interests can be provided. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's social media activity data into AI and have the AI ​​perform the task of proposing relevant routes.

[0046] The generation unit optimizes the intensity and color of the light rays by considering the surrounding environmental information when generating them. For example, the generation unit can weaken the intensity of the light rays in bright places. For example, the generation unit can strengthen the intensity of the light rays in dark places. For example, the generation unit can adjust the color of the light rays to match the surrounding color tones. In this way, by considering the surrounding environmental information, the optimal light rays can be provided. 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 surrounding environmental information into the AI ​​and have the AI ​​perform the optimization of the light ray intensity and color.

[0047] The generation unit customizes the display method of the light rays based on the user's visual characteristics when generating them. For example, the generation unit can generate light rays with easily visible colors for users with color blindness. For example, the generation unit can generate thick and clear light rays for users with reduced vision. For example, the generation unit can customize the color and intensity of the light rays according to the user's preferences. This improves visibility by generating light rays that are tailored to the user's visual characteristics. 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 visual characteristics data into the AI ​​and have the AI ​​customize the display method of the light rays.

[0048] The generation unit generates optimal rays by considering the user's geographical location information when generating rays. For example, the generation unit can generate rays along the route closest to the user's current location. For example, the generation unit can generate optimal rays by considering the means of transportation from the user's current location. For example, the generation unit can generate optimal rays by considering environmental information around the user's current location. In this way, by considering geographical location information, the optimal rays can be provided. 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 geographical location information into the AI ​​and have the AI ​​perform the generation of optimal rays.

[0049] The generation unit analyzes the user's social media activity and generates relevant rays when generating rays. For example, the generation unit can generate relevant rays based on places the user has checked into on social media. For example, the generation unit can generate relevant rays based on places the user's friends have visited. For example, the generation unit can generate relevant rays based on events or places the user has shown interest in on social media. In this way, by generating rays based on social media activity, it is possible to provide rays that match the user's interests. 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 social media activity data into AI and have the AI ​​perform the generation of relevant rays.

[0050] The display unit selects the optimal display method by referring to the user's past display history when displaying light rays. For example, the display unit can suggest the optimal display method based on the display method the user has preferred in the past. For example, the display unit can suggest a display method suitable for a specific time period based on the user's past display history. For example, the display unit can analyze the user's past display history and suggest the most efficient display method. By providing the optimal display method based on past display history, visibility is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past display history data into AI and have the AI ​​select the optimal display method.

[0051] The display unit customizes the display method based on the user's visual characteristics when displaying light rays. For example, the display unit can display light rays with easily visible colors for users with color blindness. For example, the display unit can display thick, clear light rays for users with reduced vision. For example, the display unit can customize the color and intensity of the light rays according to the user's preferences. This improves visibility by providing a display method that suits the user's visual characteristics. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user visual characteristic data into AI and have the AI ​​perform the customization of the display method.

[0052] The display unit selects the optimal display method when displaying light rays, taking into account the user's geographical location information. For example, the display unit can display light rays along the route closest to the user's current location. For example, the display unit can display the optimal light rays considering the means of transportation from the user's current location. For example, the display unit can display the optimal light rays considering the environmental information around the user's current location. In this way, the optimal display method can be provided by taking geographical location information into account. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's geographical location information into AI and have AI select the optimal display method.

[0053] The display unit analyzes the user's social media activity when displaying light rays and suggests relevant display methods. For example, the display unit can display relevant light rays based on places the user has checked into on social media. For example, the display unit can display relevant light rays based on places the user's friends have visited. For example, the display unit can display relevant light rays based on events or places the user has shown interest in on social media. In this way, by suggesting display methods based on social media activity, it is possible to provide display methods that match the user's interests. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into AI and have the AI ​​suggest relevant display methods.

[0054] The adjustment unit optimizes the intensity and color of the light rays by considering ambient environmental information during light ray adjustment. For example, the adjustment unit can weaken the light ray intensity in bright places. For example, the adjustment unit can strengthen the light ray intensity in dark places. For example, the adjustment unit can adjust the color of the light rays to match the surrounding color tones. In this way, by considering ambient environmental information, the optimal light ray can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input ambient environmental information into the AI ​​and have the AI ​​perform the optimization of the light ray intensity and color.

[0055] The adjustment unit selects the optimal adjustment method when adjusting the light rays, taking into account the user's geographical location information. For example, the adjustment unit can generate light rays along the route closest to the user's current location. For example, the adjustment unit can generate optimal light rays by taking into account the means of transportation from the user's current location. For example, the adjustment unit can generate optimal light rays by taking into account environmental information around the user's current location. In this way, by taking into account geographical location information, the optimal light rays can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal adjustment method.

[0056] The safety unit optimizes safety by considering surrounding environmental information when ensuring light ray safety. For example, the safety unit can reduce the intensity of the light rays in bright places. For example, the safety unit can increase the intensity of the light rays in dark places. For example, the safety unit can adjust the color of the light rays to match the surrounding color tones. In this way, by considering surrounding environmental information, the optimal light rays can be provided. Some or all of the above processing in the safety unit may be performed using AI, for example, or without AI. For example, the safety unit can input surrounding environmental information into the AI ​​and have the AI ​​perform the optimization of light ray intensity and color.

[0057] The safety unit selects the optimal safety assurance method when ensuring light ray safety, taking into account the user's geographical location information. For example, the safety unit can generate a light ray along the route closest to the user's current location. For example, the safety unit can generate an optimal light ray considering the user's means of transportation from their current location. For example, the safety unit can generate an optimal light ray considering environmental information around the user's current location. In this way, by considering geographical location information, the optimal light ray can be provided. Some or all of the above processing in the safety unit may be performed using AI, for example, or without AI. For example, the safety unit can input the user's geographical location information into AI and have AI select the optimal safety assurance method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception desk can analyze a user's past navigation history and automatically suggest frequently visited locations. For example, it can suggest cafes and restaurants the user has visited many times in the past. The reception desk can also analyze a user's past travel patterns and predict and suggest places they might visit on specific days or times. Furthermore, the reception desk can suggest the optimal route by considering the mode of transportation the user has used in the past (walking, cycling, driving, etc.). This allows for more personalized navigation by leveraging the user's past behavioral data.

[0060] The generation unit includes an adjustment unit that adjusts the intensity and color of the light rays. The adjustment unit can, for example, adjust the intensity of the light rays in units such as lumens or candelas. The adjustment unit can, for example, adjust the color of the light rays based on criteria such as RGB values ​​or color temperature. The adjustment unit can, for example, weaken the intensity of the light rays if the user is in a bright place. Also, the adjustment unit can, for example, strengthen the intensity of the light rays if the user is in a dark place. Furthermore, the adjustment unit can, for example, adjust the color of the light rays according to the user's preference. In this way, by adjusting the intensity and color of the light rays, it is possible to provide light rays that are easy for the user to see. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user environment information into the AI ​​and have the AI ​​perform the adjustment of the intensity and color of the light rays.

[0061] The generation unit includes a safety unit to ensure the safety of the light rays. The safety unit may include, for example, a filter to confirm that the light rays are safe for the eyes. The safety unit may also include, for example, a sensor to confirm that the light rays are safe for the skin. The safety unit can ensure safety for the eyes and skin by, for example, adjusting the intensity of the light rays. This ensures that the user can reach their destination safely by ensuring the safety of the light rays. Some or all of the above-described processes in the safety unit may be performed using, for example, AI, or not using AI. For example, the safety unit can input the intensity and color adjustment of the light rays to the AI ​​and have the AI ​​perform the safety check.

[0062] The display unit displays light rays along the ground. The display unit can display light rays along the ground using, for example, projection technology. The display unit can achieve ground-following display by, for example, adjusting the display angle and range of the light rays. This allows users to intuitively follow a route by displaying light rays along the ground. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input adjustments to the projection angle and range to the AI ​​and have the AI ​​perform the ground-following display.

[0063] The calculation unit calculates the optimal route based on map data. The calculation unit can calculate, for example, the shortest distance, the shortest time, and a route that takes traffic conditions into account. The calculation unit can optimize the route based on, for example, real-time traffic information. The calculation unit can calculate, for example, the optimal route from the user's current location to the destination. This allows the user to reach their destination efficiently by calculating the optimal route based on map data. Some or all of the above processing in the calculation unit may be performed using, for example, AI, or not using AI. For example, the calculation unit can input map data and traffic information into AI and have the AI ​​perform the calculation of the optimal route.

[0064] The reception desk analyzes the user's past destination input history and automatically suggests frequently entered destinations. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. This reduces the effort required for input by suggesting destinations based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest frequently entered destinations.

[0065] The reception desk suggests the most suitable destination based on the user's current activity status and schedule when the user enters a destination. For example, the reception desk can refer to the user's calendar information and suggest a destination based on their schedule. For example, the reception desk can suggest an appropriate destination based on the user's current activity status (e.g., exercising or resting). For example, the reception desk can analyze the user's past behavior patterns and suggest the most suitable destination for the current situation. This improves convenience by suggesting destinations that match the user's activity status and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into the AI ​​and have the AI ​​suggest the most suitable destination.

[0066] The reception unit, upon receiving destination input, prioritizes displaying highly relevant destinations by considering the user's geographical location. For example, the reception unit can prioritize displaying destinations close to the user's current location. For example, the reception unit can suggest easily accessible destinations by considering the user's means of transportation from their current location. For example, the reception unit can prioritize displaying popular spots around the user's current location. In this way, highly relevant destinations can be displayed by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​suggest highly relevant destinations.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk provides an interface for the user to enter their destination. The reception desk allows the user to enter their destination using methods such as text input, voice input, or touch input. If the user enters "I want to go to the station," the reception desk accepts that information. Step 2: The calculation unit calculates the route from the current location to the destination based on the information received by the reception unit. The calculation unit calculates the optimal route based on map data. For example, it can calculate the shortest distance, the shortest time, and a route that takes traffic conditions into consideration. Step 3: The generation unit generates a ray along the route calculated by the calculation unit. The generation unit can generate the ray using methods such as lasers, LEDs, or projection. Step 4: The display unit displays the rays generated by the generation unit. The display unit can, for example, display the rays on the screen of a mobile device. If the user is walking with the mobile device, the rays will be displayed to appear along the ground.

[0069] (Example of form 2) The navigation system according to an embodiment of the present invention is a system for people with a poor sense of direction to reach their destination without getting lost. This navigation system operates when the user launches an application built into their mobile device and inputs a destination. The application analyzes the input information and calculates the route from the current location to the destination. The application calculates the optimal route based on map data and generates a ray of light along that route. The generated ray of light appears along the ground from the mobile device. The user can reach their destination simply by following this ray of light. This allows even people with a poor sense of direction to reach their destination without getting lost. For example, the user launches an application built into their mobile device and inputs a destination. For example, they input "I want to go to the station." This information is input into the application. Next, the application analyzes the input information and calculates the route from the current location to the destination. The application calculates the optimal route based on map data and generates a ray of light along that route. For example, if the user inputs a route to the station, a ray of light along that route is generated. The generated ray of light appears along the ground from the mobile device. For example, if the user is walking with their mobile device, the ray of light appears along the ground, and the user can reach their destination simply by following the ray of light. This allows even those with a poor sense of direction to reach their destination without getting lost. The system enables users to receive intuitive navigation without complex operations. Furthermore, the beam of light appears along the ground, allowing users to walk safely. For example, if a user is walking with a mobile device, the beam of light appears along the ground, allowing the user to walk safely. This ensures that even those with a poor sense of direction can reach their destination without getting lost.

[0070] The navigation system according to the embodiment comprises a reception unit, a calculation unit, a generation unit, and a display unit. The reception unit provides an interface for the user to input a destination. The reception unit can accept the destination by methods such as text input, voice input, or touch input. For example, if the user inputs "I want to go to the station," the reception unit accepts that information. The calculation unit calculates the route from the current location to the destination based on the information accepted by the reception unit. The calculation unit calculates the optimal route based on map data. For example, the calculation unit can calculate the shortest distance, the shortest time, or a route that takes traffic conditions into consideration. For example, if the user inputs a route to the station, the calculation unit calculates that route. The generation unit generates rays of light along the route calculated by the calculation unit. The generation unit can generate rays of light by methods such as lasers, LEDs, or projection. For example, if the user inputs a route to the station, the generation unit generates rays of light along that route. The display unit displays the rays of light generated by the generation unit. The display unit can display the rays of light on the screen of a mobile device, for example. The display unit, for example, displays a beam of light appearing along the ground when the user is walking with a mobile device. This allows the navigation system according to the embodiment to reach its destination without getting lost. The elements of the reception unit, calculation unit, generation unit, and display unit operate sequentially in relation to each other. For example, the reception unit receives the destination, the calculation unit calculates the route, the generation unit generates the beam of light, and the display unit displays the beam of light. This allows the user to reach their destination simply by following the beam of light.

[0071] The reception desk provides an interface for users to input their destinations. The reception desk allows users to input destinations using methods such as text input, voice input, and touch input. Specifically, with text input, users can use a keyboard to enter the name or address of their destination. With voice input, users speak their destination into a microphone, and speech recognition technology converts what they say into text. With touch input, users can intuitively specify their destination by touching it on the map. These input methods offer multiple options to enhance user convenience, allowing users to choose the method that is easiest for them. Furthermore, the reception desk also has the ability to accurately analyze the entered destination information and complete or correct it as needed. For example, if a user enters an ambiguous place name, the reception desk will suggest alternative locations to help the user select the correct destination. In the case of voice input, filtering and noise cancellation technologies are used to improve the accuracy of speech recognition, taking into account background noise and pronunciation differences. This allows the reception desk to enable users to input destinations easily and accurately, improving the usability of the navigation system.

[0072] The calculation unit calculates the route from the user's current location to their destination based on the information received by the reception unit. The calculation unit calculates the optimal route based on map data. Specifically, the calculation unit can calculate the shortest distance, the shortest time, and routes that take traffic conditions into consideration. For example, when calculating the shortest distance route, the calculation unit identifies the route that allows the user to reach their destination in the shortest distance based on the lengths of roads and sidewalks on the map data. When calculating the shortest time route, the calculation unit identifies the route that allows the user to reach their destination in the shortest time by considering traffic conditions and waiting times at traffic lights. When calculating a route that takes traffic conditions into consideration, the calculation unit obtains real-time traffic information and identifies a route that avoids the effects of congestion and accidents. In this way, the calculation unit can provide the optimal route that meets the user's needs. Furthermore, the calculation unit can learn the user's past travel history and preferences and provide personalized route suggestions. For example, if a user has frequently used a particular route in the past, the calculation unit will prioritize suggesting that route. The calculation unit can also calculate routes according to the user's mode of transportation (walking, cycling, driving, etc.). This allows the calculation unit to provide flexible route suggestions tailored to the user's travel style and preferences, thereby improving the convenience of the navigation system.

[0073] The generation unit generates a ray of light along the route calculated by the calculation unit. The generation unit can generate the ray using methods such as lasers, LEDs, and projection. Specifically, when using a laser, the generation unit uses high-precision laser light to project a ray indicating the route onto the ground or a wall. When using LEDs, the generation unit combines multiple LEDs to form a line of light along the route. When using projection, the generation unit uses a projector to project navigation information such as maps and arrows onto the ground or a wall. This allows the user to visually confirm the route as they move. Furthermore, the generation unit also has a function to adjust the color and brightness of the ray. For example, in bright daytime environments, the brightness of the ray is increased, and in nighttime or dark environments, the color of the ray is changed to improve visibility. In addition, the generation unit can adjust the display position and direction of the ray in real time according to the user's movement speed and direction. This allows the user to always confirm the route with optimal visibility. Furthermore, the generation unit has a function to generate individual rays to accommodate simultaneous use by multiple users. This allows the generation unit to provide visually easy-to-understand navigation information so that users can reach their destination without getting lost.

[0074] The display unit displays the light rays generated by the generation unit. For example, the display unit can display the light rays on the screen of a mobile device. Specifically, it uses augmented reality (AR) technology, which overlays the light rays onto the real-world scenery using the mobile device's camera. This allows the user to navigate while viewing the light rays aligned with the real-world scenery through the mobile device's screen. For example, if a user is walking with a mobile device, the light rays will appear to appear along the ground, allowing the user to reach their destination simply by following them. Furthermore, the display unit also has a function to customize how the light rays are displayed. For example, if a user has color blindness, the color of the light rays can be changed to improve visibility. The display unit can also change the thickness and shape of the light rays according to the user's preference. This allows the display unit to provide flexible displays tailored to the individual needs of the user. Additionally, the display unit can provide navigation information through non-visual means by combining it with additional functions such as voice guidance and vibration notifications. For example, if a user loses sight of the light rays, voice guidance can direct them to the next direction, or vibration notifications can alert them. This allows the display unit to provide visual and auditory navigation information so that users can reach their destination without getting lost.

[0075] The generation unit includes an adjustment unit that adjusts the intensity and color of the light rays. The adjustment unit can, for example, adjust the intensity of the light rays in units such as lumens or candelas. The adjustment unit can, for example, adjust the color of the light rays based on criteria such as RGB values ​​or color temperature. The adjustment unit can, for example, weaken the intensity of the light rays if the user is in a bright place. Also, the adjustment unit can, for example, strengthen the intensity of the light rays if the user is in a dark place. Furthermore, the adjustment unit can, for example, adjust the color of the light rays according to the user's preference. In this way, by adjusting the intensity and color of the light rays, it is possible to provide light rays that are easy for the user to see. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user environment information into the AI ​​and have the AI ​​perform the adjustment of the intensity and color of the light rays.

[0076] The generation unit includes a safety unit to ensure the safety of the light rays. The safety unit may include, for example, a filter to confirm that the light rays are safe for the eyes. The safety unit may also include, for example, a sensor to confirm that the light rays are safe for the skin. The safety unit can ensure safety for the eyes and skin by, for example, adjusting the intensity of the light rays. This ensures that the user can reach their destination safely by ensuring the safety of the light rays. Some or all of the above-described processes in the safety unit may be performed using, for example, AI, or not using AI. For example, the safety unit can input the intensity and color adjustment of the light rays to the AI ​​and have the AI ​​perform the safety check.

[0077] The display unit displays light rays along the ground. The display unit can display light rays along the ground using, for example, projection technology. The display unit can achieve ground-following display by, for example, adjusting the display angle and range of the light rays. This allows users to intuitively follow a route by displaying light rays along the ground. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input adjustments to the projection angle and range to the AI ​​and have the AI ​​perform the ground-following display.

[0078] The calculation unit calculates the optimal route based on map data. The calculation unit can calculate, for example, the shortest distance, the shortest time, and a route that takes traffic conditions into account. The calculation unit can optimize the route based on, for example, real-time traffic information. The calculation unit can calculate, for example, the optimal route from the user's current location to the destination. This allows the user to reach their destination efficiently by calculating the optimal route based on map data. Some or all of the above processing in the calculation unit may be performed using, for example, AI, or not using AI. For example, the calculation unit can input map data and traffic information into AI and have the AI ​​perform the calculation of the optimal route.

[0079] The reception unit estimates the user's emotions and customizes the destination input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick destination input. This makes destination input easier by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception desk analyzes the user's past destination input history and automatically suggests frequently entered destinations. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. This reduces the effort required for input by suggesting destinations based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest frequently entered destinations.

[0081] The reception desk suggests the most suitable destination based on the user's current activity status and schedule when the user enters a destination. For example, the reception desk can refer to the user's calendar information and suggest a destination based on their schedule. For example, the reception desk can suggest an appropriate destination based on the user's current activity status (e.g., exercising or resting). For example, the reception desk can analyze the user's past behavior patterns and suggest the most suitable destination for the current situation. This improves convenience by suggesting destinations that match the user's activity status and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into the AI ​​and have the AI ​​suggest the most suitable destination.

[0082] The reception desk estimates the user's emotions and determines the priority of the entered destinations based on the estimated emotions. For example, if the user is in a hurry, the reception desk can prioritize displaying the nearest destination. For example, if the user is relaxed, the reception desk can prioritize displaying tourist attractions or places where the user can relax. For example, if the user is stressed, the reception desk can prioritize displaying places suitable for stress relief. In this way, appropriate destinations can be displayed by setting priorities 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 reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception unit, upon receiving destination input, prioritizes displaying highly relevant destinations by considering the user's geographical location. For example, the reception unit can prioritize displaying destinations close to the user's current location. For example, the reception unit can suggest easily accessible destinations by considering the user's means of transportation from their current location. For example, the reception unit can prioritize displaying popular spots around the user's current location. In this way, highly relevant destinations can be displayed by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​suggest highly relevant destinations.

[0084] The reception desk analyzes the user's social media activity when a destination is entered and suggests relevant destinations. For example, the reception desk can suggest relevant destinations based on places the user has checked in to on social media. For example, the reception desk can suggest relevant destinations based on places the user's friends have visited. For example, the reception desk can suggest relevant destinations based on events or places the user has shown interest in on social media. This allows the system to display destinations that match the user's interests by suggesting destinations based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​suggest relevant destinations.

[0085] The calculation unit estimates the user's emotions and adjusts the route calculation algorithm based on the estimated emotions. For example, if the user is in a hurry, the calculation unit can use an algorithm that prioritizes the shortest route. For example, if the user is relaxed, the calculation unit can use an algorithm that prioritizes a scenic route. For example, if the user is stressed, the calculation unit can use an algorithm that prioritizes a route that avoids congestion. This allows the system to provide the optimal route by performing route calculations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The calculation unit predicts the optimal route by referring to past route data during route calculation. For example, the calculation unit can suggest the optimal route based on routes previously used by the user. For example, the calculation unit can suggest a route that avoids congestion based on the user's past travel history. For example, the calculation unit can analyze the user's past travel history and suggest the most efficient route. In this way, by predicting the optimal route based on past route data, an efficient route can be provided. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past route data into AI and have the AI ​​perform the prediction of the optimal route.

[0087] The calculation unit optimizes routes by considering traffic conditions and weather information during route calculation. For example, the calculation unit can propose the optimal route based on real-time traffic congestion information. For example, the calculation unit can propose the optimal route by considering the real-time operating status of public transportation. For example, the calculation unit can propose the optimal route based on real-time weather information. In this way, the optimal route can be provided by considering traffic conditions and weather information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input traffic conditions and weather information into AI and have the AI ​​perform the calculation of the optimal route.

[0088] The calculation unit estimates the user's emotions and determines the priority of routes calculated based on the estimated user emotions. For example, if the user is in a hurry, the calculation unit can prioritize displaying the shortest route. For example, if the user is relaxed, the calculation unit can prioritize displaying a route with good scenery. For example, if the user is stressed, the calculation unit can prioritize displaying a route that avoids congestion. In this way, by setting route priorities according to the user's emotions, an appropriate route can be provided. 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 calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The calculation unit proposes the optimal route by considering the user's geographical location information during route calculation. For example, the calculation unit can propose the route closest to the user's current location. For example, the calculation unit can propose the optimal route by considering the means of transportation from the user's current location. For example, the calculation unit can propose the optimal route by considering the traffic conditions around the user's current location. In this way, the optimal route can be provided by considering geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information into AI and have AI perform the calculation of the optimal route.

[0090] The calculation unit analyzes the user's social media activity and proposes relevant routes when calculating routes. For example, the calculation unit can propose relevant routes based on places the user has checked in to on social media. For example, the calculation unit can propose relevant routes based on places the user's friends have visited. For example, the calculation unit can propose relevant routes based on events or places the user has shown interest in on social media. In this way, by proposing routes based on social media activity, routes that match the user's interests can be provided. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's social media activity data into AI and have the AI ​​perform the task of proposing relevant routes.

[0091] The generation unit estimates the user's emotions and adjusts the ray generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate soft-colored rays. For example, if the user is in a hurry, the generation unit can generate bright, clear rays. For example, if the user is stressed, the generation unit can generate calm-colored rays. This improves visibility by generating rays that correspond 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, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The generation unit optimizes the intensity and color of the light rays by considering the surrounding environmental information when generating them. For example, the generation unit can weaken the intensity of the light rays in bright places. For example, the generation unit can strengthen the intensity of the light rays in dark places. For example, the generation unit can adjust the color of the light rays to match the surrounding color tones. In this way, by considering the surrounding environmental information, the optimal light rays can be provided. 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 surrounding environmental information into the AI ​​and have the AI ​​perform the optimization of the light ray intensity and color.

[0093] The generation unit customizes the display method of the light rays based on the user's visual characteristics when generating them. For example, the generation unit can generate light rays with easily visible colors for users with color blindness. For example, the generation unit can generate thick and clear light rays for users with reduced vision. For example, the generation unit can customize the color and intensity of the light rays according to the user's preferences. This improves visibility by generating light rays that are tailored to the user's visual characteristics. 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 visual characteristics data into the AI ​​and have the AI ​​customize the display method of the light rays.

[0094] The generation unit estimates the user's emotions and determines the priority of the generated light rays based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can prioritize displaying the brightest light rays. For example, if the user is relaxed, the generation unit can prioritize displaying light rays with softer tones. For example, if the user is stressed, the generation unit can prioritize displaying light rays with calmer tones. In this way, appropriate light rays can be provided by setting the priority of light rays 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, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0095] The generation unit generates optimal rays by considering the user's geographical location information when generating rays. For example, the generation unit can generate rays along the route closest to the user's current location. For example, the generation unit can generate optimal rays by considering the means of transportation from the user's current location. For example, the generation unit can generate optimal rays by considering environmental information around the user's current location. In this way, by considering geographical location information, the optimal rays can be provided. 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 geographical location information into the AI ​​and have the AI ​​perform the generation of optimal rays.

[0096] The generation unit analyzes the user's social media activity and generates relevant rays when generating rays. For example, the generation unit can generate relevant rays based on places the user has checked into on social media. For example, the generation unit can generate relevant rays based on places the user's friends have visited. For example, the generation unit can generate relevant rays based on events or places the user has shown interest in on social media. In this way, by generating rays based on social media activity, it is possible to provide rays that match the user's interests. 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 social media activity data into AI and have the AI ​​perform the generation of relevant rays.

[0097] The display unit estimates the user's emotions and adjusts the display method of the light rays based on the estimated user emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the display unit can provide a display method that gets straight to the point. This improves visibility by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0098] The display unit selects the optimal display method by referring to the user's past display history when displaying light rays. For example, the display unit can suggest the optimal display method based on the display method the user has preferred in the past. For example, the display unit can suggest a display method suitable for a specific time period based on the user's past display history. For example, the display unit can analyze the user's past display history and suggest the most efficient display method. By providing the optimal display method based on past display history, visibility is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past display history data into AI and have the AI ​​select the optimal display method.

[0099] The display unit customizes the display method based on the user's visual characteristics when displaying light rays. For example, the display unit can display light rays with easily visible colors for users with color blindness. For example, the display unit can display thick, clear light rays for users with reduced vision. For example, the display unit can customize the color and intensity of the light rays according to the user's preferences. This improves visibility by providing a display method that suits the user's visual characteristics. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user visual characteristic data into AI and have the AI ​​perform the customization of the display method.

[0100] The display unit estimates the user's emotions and adjusts the display order of the light rays based on the estimated emotions. For example, if the user is in a hurry, the display unit can prioritize displaying the most important information. For example, if the user is relaxed, the display unit can prioritize displaying detailed information in a sequential manner. For example, if the user is stressed, the display unit can prioritize displaying visually calming information. This improves visibility by providing a display order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The display unit selects the optimal display method when displaying light rays, taking into account the user's geographical location information. For example, the display unit can display light rays along the route closest to the user's current location. For example, the display unit can display the optimal light rays considering the means of transportation from the user's current location. For example, the display unit can display the optimal light rays considering the environmental information around the user's current location. In this way, the optimal display method can be provided by taking geographical location information into account. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's geographical location information into AI and have AI select the optimal display method.

[0102] The display unit analyzes the user's social media activity when displaying light rays and suggests relevant display methods. For example, the display unit can display relevant light rays based on places the user has checked into on social media. For example, the display unit can display relevant light rays based on places the user's friends have visited. For example, the display unit can display relevant light rays based on events or places the user has shown interest in on social media. In this way, by suggesting display methods based on social media activity, it is possible to provide display methods that match the user's interests. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into AI and have the AI ​​suggest relevant display methods.

[0103] The adjustment unit estimates the user's emotions and adjusts the intensity and color of the light rays based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can generate soft-toned light rays. For example, if the user is in a hurry, the adjustment unit can generate bright, clear light rays. For example, if the user is stressed, the adjustment unit can generate calm-toned light rays. This improves visibility by providing light ray intensity and color that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The adjustment unit optimizes the intensity and color of the light rays by considering ambient environmental information during light ray adjustment. For example, the adjustment unit can weaken the light ray intensity in bright places. For example, the adjustment unit can strengthen the light ray intensity in dark places. For example, the adjustment unit can adjust the color of the light rays to match the surrounding color tones. In this way, by considering ambient environmental information, the optimal light ray can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input ambient environmental information into the AI ​​and have the AI ​​perform the optimization of the light ray intensity and color.

[0105] The adjustment unit estimates the user's emotions and determines how to adjust the light rays based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can generate soft-toned light rays. For example, if the user is in a hurry, the adjustment unit can generate bright, clear light rays. For example, if the user is stressed, the adjustment unit can generate calm-toned light rays. This improves visibility by providing a light ray adjustment method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0106] The adjustment unit selects the optimal adjustment method when adjusting the light rays, taking into account the user's geographical location information. For example, the adjustment unit can generate light rays along the route closest to the user's current location. For example, the adjustment unit can generate optimal light rays by taking into account the means of transportation from the user's current location. For example, the adjustment unit can generate optimal light rays by taking into account environmental information around the user's current location. In this way, by taking into account geographical location information, the optimal light rays can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal adjustment method.

[0107] The safety unit estimates the user's emotions and adjusts the method of ensuring the safety of the light beam based on the estimated user emotions. For example, if the user is tense, the safety unit can reduce the intensity of the light beam to alleviate visual strain. For example, if the user is relaxed, the safety unit can maintain an appropriate intensity of the light beam. For example, if the user is in a hurry, the safety unit can increase the intensity of the light beam to improve visibility. This improves visibility by providing safety tailored 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 safety unit may be performed using AI or not using AI. For example, the safety unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The safety unit optimizes safety by considering surrounding environmental information when ensuring light ray safety. For example, the safety unit can reduce the intensity of the light rays in bright places. For example, the safety unit can increase the intensity of the light rays in dark places. For example, the safety unit can adjust the color of the light rays to match the surrounding color tones. In this way, by considering surrounding environmental information, the optimal light rays can be provided. Some or all of the above processing in the safety unit may be performed using AI, for example, or without AI. For example, the safety unit can input surrounding environmental information into the AI ​​and have the AI ​​perform the optimization of light ray intensity and color.

[0109] The safety unit estimates the user's emotions and determines priorities for ensuring the safety of the light beam based on the estimated user emotions. For example, if the user is tense, the safety unit can reduce the intensity of the light beam to alleviate visual strain. For example, if the user is relaxed, the safety unit can maintain an appropriate intensity of the light beam. For example, if the user is in a hurry, the safety unit can increase the intensity of the light beam to improve visibility. This improves visibility by providing safety tailored 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 safety unit may be performed using AI, for example, or without AI. For example, the safety unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0110] The safety unit selects the optimal safety assurance method when ensuring light ray safety, taking into account the user's geographical location information. For example, the safety unit can generate a light ray along the route closest to the user's current location. For example, the safety unit can generate an optimal light ray considering the user's means of transportation from their current location. For example, the safety unit can generate an optimal light ray considering environmental information around the user's current location. In this way, by considering geographical location information, the optimal light ray can be provided. Some or all of the above processing in the safety unit may be performed using AI, for example, or without AI. For example, the safety unit can input the user's geographical location information into AI and have AI select the optimal safety assurance method.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception desk can analyze a user's past navigation history and automatically suggest frequently visited locations. For example, it can suggest cafes and restaurants the user has visited many times in the past. The reception desk can also analyze a user's past travel patterns and predict and suggest places they might visit on specific days or times. Furthermore, the reception desk can suggest the optimal route by considering the mode of transportation the user has used in the past (walking, cycling, driving, etc.). This allows for more personalized navigation by leveraging the user's past behavioral data.

[0113] The generation unit includes an adjustment unit that adjusts the intensity and color of the light rays. The adjustment unit can, for example, adjust the intensity of the light rays in units such as lumens or candelas. The adjustment unit can, for example, adjust the color of the light rays based on criteria such as RGB values ​​or color temperature. The adjustment unit can, for example, weaken the intensity of the light rays if the user is in a bright place. Also, the adjustment unit can, for example, strengthen the intensity of the light rays if the user is in a dark place. Furthermore, the adjustment unit can, for example, adjust the color of the light rays according to the user's preference. In this way, by adjusting the intensity and color of the light rays, it is possible to provide light rays that are easy for the user to see. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user environment information into the AI ​​and have the AI ​​perform the adjustment of the intensity and color of the light rays.

[0114] The generation unit includes a safety unit to ensure the safety of the light rays. The safety unit may include, for example, a filter to confirm that the light rays are safe for the eyes. The safety unit may also include, for example, a sensor to confirm that the light rays are safe for the skin. The safety unit can ensure safety for the eyes and skin by, for example, adjusting the intensity of the light rays. This ensures that the user can reach their destination safely by ensuring the safety of the light rays. Some or all of the above-described processes in the safety unit may be performed using, for example, AI, or not using AI. For example, the safety unit can input the intensity and color adjustment of the light rays to the AI ​​and have the AI ​​perform the safety check.

[0115] The display unit displays light rays along the ground. The display unit can display light rays along the ground using, for example, projection technology. The display unit can achieve ground-following display by, for example, adjusting the display angle and range of the light rays. This allows users to intuitively follow a route by displaying light rays along the ground. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input adjustments to the projection angle and range to the AI ​​and have the AI ​​perform the ground-following display.

[0116] The calculation unit calculates the optimal route based on map data. The calculation unit can calculate, for example, the shortest distance, the shortest time, and a route that takes traffic conditions into account. The calculation unit can optimize the route based on, for example, real-time traffic information. The calculation unit can calculate, for example, the optimal route from the user's current location to the destination. This allows the user to reach their destination efficiently by calculating the optimal route based on map data. Some or all of the above processing in the calculation unit may be performed using, for example, AI, or not using AI. For example, the calculation unit can input map data and traffic information into AI and have the AI ​​perform the calculation of the optimal route.

[0117] The reception unit estimates the user's emotions and customizes the destination input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick destination input. This makes destination input easier by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0118] The reception desk analyzes the user's past destination input history and automatically suggests frequently entered destinations. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. This reduces the effort required for input by suggesting destinations based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​suggest frequently entered destinations.

[0119] The reception desk suggests the most suitable destination based on the user's current activity status and schedule when the user enters a destination. For example, the reception desk can refer to the user's calendar information and suggest a destination based on their schedule. For example, the reception desk can suggest an appropriate destination based on the user's current activity status (e.g., exercising or resting). For example, the reception desk can analyze the user's past behavior patterns and suggest the most suitable destination for the current situation. This improves convenience by suggesting destinations that match the user's activity status and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into the AI ​​and have the AI ​​suggest the most suitable destination.

[0120] The reception desk estimates the user's emotions and determines the priority of the entered destinations based on the estimated emotions. For example, if the user is in a hurry, the reception desk can prioritize displaying the nearest destination. For example, if the user is relaxed, the reception desk can prioritize displaying tourist attractions or places where the user can relax. For example, if the user is stressed, the reception desk can prioritize displaying places suitable for stress relief. In this way, appropriate destinations can be displayed by setting priorities 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 reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0121] The reception unit, upon receiving destination input, prioritizes displaying highly relevant destinations by considering the user's geographical location. For example, the reception unit can prioritize displaying destinations close to the user's current location. For example, the reception unit can suggest easily accessible destinations by considering the user's means of transportation from their current location. For example, the reception unit can prioritize displaying popular spots around the user's current location. In this way, highly relevant destinations can be displayed by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​suggest highly relevant destinations.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk provides an interface for the user to enter their destination. The reception desk allows the user to enter their destination using methods such as text input, voice input, or touch input. If the user enters "I want to go to the station," the reception desk accepts that information. Step 2: The calculation unit calculates the route from the current location to the destination based on the information received by the reception unit. The calculation unit calculates the optimal route based on map data. For example, it can calculate the shortest distance, the shortest time, and a route that takes traffic conditions into consideration. Step 3: The generation unit generates a ray along the route calculated by the calculation unit. The generation unit can generate the ray using methods such as lasers, LEDs, or projection. Step 4: The display unit displays the rays generated by the generation unit. The display unit can, for example, display the rays on the screen of a mobile device. If the user is walking with the mobile device, the rays will be displayed to appear along the ground.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the reception unit, calculation unit, generation unit, display unit, adjustment unit, and safety unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input a destination. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the route from the current location to the destination. The generation unit is implemented by the control unit 46A of the smart device 14 and generates a ray of light along the calculated route. The display unit is implemented by the output device 40 of the smart device 14 and displays the generated ray along the ground. The adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts the intensity and color of the ray of light. The safety unit is implemented by the specific processing unit 290 of the data processing unit 12 and ensures the safety of the ray of light. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the reception unit, calculation unit, generation unit, display unit, adjustment unit, and safety unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input a destination. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the route from the current location to the destination. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates a ray of light along the calculated route. The display unit is implemented by the output device 40 of the smart glasses 214 and displays the generated ray along the ground. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the intensity and color of the ray. The safety unit is implemented by the specific processing unit 290 of the data processing unit 12 and ensures the safety of the ray. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, calculation unit, generation unit, display unit, adjustment unit, and safety unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input a destination. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the route from the current location to the destination. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates a ray of light along the calculated route. The display unit is implemented by the output device 40 of the headset terminal 314 and displays the generated ray along the ground. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the intensity and color of the ray of light. The safety unit is implemented by the specific processing unit 290 of the data processing unit 12 and ensures the safety of the ray of light. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the reception unit, calculation unit, generation unit, display unit, adjustment unit, and safety unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input a destination. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the route from the current location to the destination. The generation unit is implemented by the control unit 46A of the robot 414 and generates a ray of light along the calculated route. The display unit is implemented by the output device 40 of the robot 414 and displays the generated ray along the ground. The adjustment unit is implemented by the control unit 46A of the robot 414 and adjusts the intensity and color of the ray of light. The safety unit is implemented by the specific processing unit 290 of the data processing unit 12 and ensures the safety of the ray of light. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A reception desk that accepts destination inputs, A calculation unit that calculates a route based on the information received by the reception unit, A generation unit that generates a ray along the route calculated by the calculation unit, The system includes a display unit that displays the light rays generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It features an adjustment unit for adjusting the intensity and color of the light rays. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Equipped with a safety section to ensure the safety of the light beam. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Display the light rays along the ground. The system described in Appendix 1, characterized by the features described herein. (Note 5) The calculation unit, Calculate the optimal route based on map data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and customizes the destination input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the user's past destination input history and automatically suggests frequently entered destinations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When you enter a destination, the system will suggest the most suitable destination based on your current activities and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered destinations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering a destination, the system prioritizes displaying the most relevant destinations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a destination is entered, the system analyzes the user's social media activity and suggests relevant destinations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The calculation unit, The system estimates the user's emotions and adjusts the root calculation algorithm based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The calculation unit, When calculating a route, the system predicts the optimal route by referring to past route data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit, When calculating a route, the system optimizes the route by taking into account traffic conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit, It estimates the user's emotions and determines route priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, When calculating a route, the system proposes the optimal route considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, During route calculation, the system analyzes the user's social media activity and suggests relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the ray 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 light rays, the intensity and color of the rays are optimized by taking into account information about the surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating light rays, customize how the rays are displayed based on the user's visual characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of the generated rays based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating light rays, the system takes the user's geographical location information into consideration to generate the optimal light ray. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating rays, the system analyzes the user's social media activity and generates relevant rays. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is It estimates the user's emotions and adjusts the way the light rays are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying light rays, the system selects the optimal display method by referring to the user's past display history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying light rays, the display method is customized based on the user's visual characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and adjusts the display order of the light rays based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying light rays, the optimal display method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying light beams, the system analyzes the user's social media activity and suggests relevant display methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, It estimates the user's emotions and adjusts the intensity and color of the light based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The adjustment unit is, When adjusting the lighting, the intensity and color of the light are optimized by taking into account information about the surrounding environment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The adjustment unit is, The system estimates the user's emotions and determines how to adjust the lighting based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The adjustment unit is, When adjusting the lighting, the optimal adjustment method is selected by considering the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned safety unit is We estimate the user's emotions and adjust the method of ensuring ray safety based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned safety unit is When ensuring light safety, optimize safety by considering surrounding environmental information. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned safety unit is The system estimates the user's emotions and determines priorities for ensuring the safety of the light rays based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned safety unit is When ensuring light safety, the optimal safety method is selected by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0196] 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 reception desk that accepts destination inputs, A calculation unit that calculates a route based on the information received by the reception unit, A generation unit that generates a ray along the route calculated by the calculation unit, The system includes a display unit that displays the light rays generated by the generation unit. A system characterized by the following features.

2. The generating unit is It features an adjustment unit for adjusting the intensity and color of the light rays. The system according to feature 1.

3. The generating unit is Equipped with a safety section to ensure the safety of the light beam. The system according to feature 1.

4. The aforementioned display unit is Display the light rays along the ground. The system according to feature 1.

5. The calculation unit, Calculate the optimal route based on map data The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and customizes the destination input interface based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past destination input history and automatically suggests frequently entered destinations. The system according to feature 1.

8. The aforementioned reception unit is When you enter a destination, the system will suggest the most suitable destination based on your current activities and schedule. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered destinations based on the estimated user emotions. The system according to feature 1.

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