System

Abstract

An object of a system according to an embodiment is to provide an efficient travel route for a business meeting or work in a new city.SOLUTION: A system includes a reception unit, an analysis unit, and a navigation unit. The reception part inputs a destination and details of the meeting. The analysis unit analyzes the information input by the reception unit and calculates an efficient moving route. The navigation unit displays the route calculated by the analysis unit and provides navigation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it can be difficult to find efficient routes to get to business meetings or work in a new city.

[0005] The system according to the embodiment aims to provide efficient travel routes for business meetings or work in a new city. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a navigation unit. The reception unit inputs details of a destination and a meeting. The analysis unit analyzes the information input by the reception unit and calculates an efficient travel route. The navigation unit displays the route calculated by the analysis unit and provides navigation. [Effects of the Invention]

[0007] The system according to the embodiment can provide efficient travel routes for business meetings or work in a new city. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A business meeting navigation system according to an embodiment of the present invention allows users to travel efficiently in a new city. The system includes a reception unit for inputting details of a destination and a meeting; an analysis unit for analyzing the information input by the reception unit and calculating an efficient travel route; and a navigation unit for displaying the route calculated by the analysis unit and providing navigation. For example, when a user inputs details of a destination and a meeting, the LLM analyzes the input information and calculates the optimal travel route. The calculated route is displayed on the AR glasses, allowing the user to receive visual navigation. This allows users to travel efficiently in a new city without getting lost. Furthermore, the AR glasses can provide real-time information while traveling. For example, the glasses can display the next train departure time and information about facilities near the destination. This allows the business meeting navigation system to support users in traveling efficiently in a new city. For example, it can provide support for arriving at an important meeting on time. This is particularly effective for business professionals, as it can save time and reduce stress.

[0029] A business meeting navigation system according to an embodiment includes a reception unit, an analysis unit, and a navigation unit. The reception unit allows a user to input details of a destination and a meeting. For example, the reception unit allows a user to input information such as the location and start time of the meeting and participant information. The reception unit can accept, for example, text input or voice input. The reception unit can also estimate a user's emotions and adjust the input method based on the estimated user emotions. For example, if a user is feeling stressed, a simple interface can be provided to minimize the input steps. The analysis unit analyzes the information input by the reception unit and calculates an efficient travel route. For example, the analysis unit can propose an optimal route taking into account traffic conditions, weather, the user's schedule, and the like. The analysis unit can, for example, propose a route to avoid traffic congestion or a means of transportation appropriate for the weather. The navigation unit displays the route calculated by the analysis unit and provides navigation. For example, the navigation unit provides navigation by following arrows and guide displays displayed on the AR glasses. The navigation unit can also provide, for example, real-time information on the departure time of the next train and information on facilities around the destination while traveling. As a result, the business meeting navigation system according to the embodiment can support the user in efficiently traveling in a new city.

[0030] The reception unit can analyze the user's past input history and suggest an efficient input method. For example, the reception unit automatically displays destinations or meeting details that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations or meeting details to be used during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0031] The reception unit can use voice recognition and gesture recognition to assist input when entering details of a destination or meeting. For example, the reception unit can automatically set the destination when the user simply inputs "location of next meeting" by voice. The reception unit can also allow the user to easily set the details of the destination or meeting by performing a specific gesture on the screen of the AR glasses. Furthermore, the reception unit can also allow the user to combine voice input and gesture input to more intuitively set the details of the destination or meeting. This improves input efficiency by using voice recognition and gesture recognition.

[0032] The reception unit can automatically acquire the user's current location information when entering details of a destination or meeting, simplifying the input process. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The reception unit can also suggest optimal candidate locations taking into account the distance from the user's current location when the user enters a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the user's current location in real time and reflect it as the departure point. This automatically acquires the user's current location information, eliminating the need for input.

[0033] The reception unit can automatically suggest candidate locations by referring to the user's past travel history when inputting details of a destination or meeting. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest optimal candidate locations. This improves input efficiency by suggesting candidate locations based on the user's past travel history.

[0034] When inputting details of a destination or meeting, the reception unit can refer to the user's calendar information and make suggestions based on the schedule. For example, the reception unit can refer to the schedule registered in the user's calendar and automatically set the destination and meeting details. The reception unit can also suggest locations related to specific events as candidate locations based on the user's calendar information. Furthermore, the reception unit can also suggest the optimal route based on the schedule based on the user's calendar information. This improves input efficiency by making suggestions based on calendar information.

[0035] The reception unit can analyze the user's social media activity and suggest related places when inputting details of a destination or meeting. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. This improves the efficiency of input by suggesting related places based on social media activity.

[0036] The analysis unit can calculate an efficient travel route by taking into account traffic conditions, weather, the user's schedule, and the user's health condition. For example, if the user is tired, the analysis unit can suggest the shortest route. Also, if the user is seeking healthy exercise, the analysis unit can suggest a slightly longer route. Furthermore, if the user is feeling unwell, the analysis unit can suggest a route that includes rest points. This improves travel efficiency by providing a route that takes into account the user's health condition.

[0037] During analysis, the analysis unit can propose hybrid routes that combine multiple means of transportation. For example, the analysis unit can propose a route that combines trains and buses. The analysis unit can also propose a route that combines taxis and bicycles. Furthermore, the analysis unit can also propose a route that combines trains and walking. This improves travel efficiency by combining multiple means of transportation.

[0038] During analysis, the analysis unit can refer to the user's past travel history and prioritize suggesting a preferred route. The analysis unit can, for example, suggest an optimal route based on routes the user has used in the past. The analysis unit can also suggest a route that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most efficient route. This improves travel efficiency by suggesting a preferred route based on the user's past travel history.

[0039] During analysis, the analysis unit can propose a route taking into consideration the user's geographical preferences. For example, the analysis unit can prioritize scenic routes. The analysis unit can also prioritize quiet routes. Furthermore, the analysis unit can propose an optimal route based on the user's past selection history. This improves travel efficiency by providing a route that takes into consideration the user's geographical preferences.

[0040] During the analysis, the analysis unit can take into consideration the user's eco-consciousness and prioritize suggesting environmentally friendly means of transportation. For example, the analysis unit can prioritize public transportation. The analysis unit can also prioritize bicycles and walking. Furthermore, the analysis unit can also suggest routes that combine environmentally friendly means of transportation. This improves travel efficiency by providing environmentally friendly means of transportation.

[0041] The analysis unit can continuously improve the route calculation algorithm by reflecting user feedback during analysis. For example, the analysis unit improves the route calculation algorithm based on user feedback. The analysis unit can also analyze the user's past feedback and propose an optimal route. Furthermore, the analysis unit can reflect user feedback in real time and improve the route calculation algorithm. In this way, by reflecting feedback, the accuracy of route calculation is improved.

[0042] The navigation unit can adjust the timing of guidance during navigation according to the user's walking speed and means of transportation. For example, if the user changes their walking speed, the navigation unit adjusts the navigation progress speed in real time. The navigation unit can also adjust the timing of navigation guidance in real time if the user changes their means of transportation. Furthermore, the navigation unit can pause navigation when the user stops and resume it when the user starts walking again. This improves the efficiency of travel by providing guidance timing according to the user's walking speed and means of transportation.

[0043] The navigation unit can track the user's gaze during navigation and optimize the guidance display according to the direction of the gaze. For example, when the user directs their gaze in a specific direction, the navigation unit can emphasize the guidance display related to that direction. Furthermore, when the user moves their gaze, the navigation unit can also update the guidance display in real time in accordance with the movement of the gaze. Furthermore, when the user fixes their gaze, the navigation unit can display detailed information related to the direction of the gaze. This improves the efficiency of travel by providing guidance display according to the direction of the gaze.

[0044] The navigation unit can update the user's current location information in real time during navigation and recalculate the optimal route. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the user's current location in real time and suggest the optimal route as the user approaches the destination. Furthermore, if the user gets lost, the navigation unit can update the user's current location in real time and perform navigation again. This allows the optimal route to be provided by updating the current location information in real time.

[0045] The navigation unit can provide guidance in cooperation with the user's device information during navigation. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Also, if the user is using a smartwatch, the navigation unit can provide a simple and highly visible display method. Furthermore, if the user is using a tablet, the navigation unit can provide a display method that is optimized for a large screen. This allows optimal guidance to be provided by coordinating with the device information.

[0046] The navigation unit can customize the theme and color of the guidance display according to the user's preferences during navigation. For example, the navigation unit customizes the guidance display by selecting a theme with a color scheme that the user prefers. The navigation unit can also customize the guidance display by selecting a font style that the user prefers. Furthermore, the navigation unit can suggest optimal themes and colors based on the user's past selection history. This improves the efficiency of travel by providing guidance displays that meet the user's preferences.

[0047] During navigation, the navigation unit can refer to the user's past travel history and prioritize displaying frequently used routes. For example, the navigation unit can prioritize displaying routes that the user has frequently used in the past. The navigation unit can also prioritize displaying routes that avoid congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and prioritize displaying the most efficient route. This improves travel efficiency by providing optimal routes based on the user's past travel history.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can also monitor the user's health condition and adjust the input method. For example, if the user is tired, voice input can be prioritized to reduce the burden of manual input. Also, if the user is in good health, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in poor health, the reception unit can provide a simple interface to enable quick completion of input. This improves input efficiency by providing input methods according to the user's health condition.

[0050] The analysis unit can analyze the user's past travel history and suggest rest points during travel. For example, it can suggest cafes or restaurants that the user has frequently visited in the past as rest points. Also, if the user tends to take rests at specific times of the day, it can suggest rest points that match those times. Furthermore, it can predict and suggest optimal rest points based on the user's past travel history. This improves travel efficiency by suggesting rest points based on the user's past travel history.

[0051] The analysis unit can adjust the amount of exercise during travel taking into account the user's health condition. For example, if the user is seeking healthy exercise, it can suggest a route that includes walking or cycling. Also, if the user is tired, it can suggest the shortest route to minimize the amount of exercise. Furthermore, if the user is feeling unwell, it can suggest a route that includes rest points. This improves travel efficiency by providing a route that takes into account the user's health condition.

[0052] The navigation unit can refer to the user's past travel history and prioritize the display of frequently used routes. For example, it can prioritize the display of routes that the user has used frequently in the past. It can also prioritize the display of routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and prioritize the display of the most efficient route. This improves travel efficiency by providing optimal routes based on the user's past travel history.

[0053] The navigation unit can provide guidance in cooperation with the user's device information. For example, if the user is using a smartphone, a display method suited to the screen size can be provided. Also, if the user is using a smartwatch, a simple and highly visible display method can be provided. Furthermore, if the user is using a tablet, a display method optimized for a large screen can be provided. In this way, by coordinating with the device information, optimal guidance can be provided.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit allows the user to input details of the destination and meeting. For example, the reception unit allows the user to input the location and start time of the meeting, participant information, etc. The reception unit can accept, for example, text input or voice input. The reception unit can also estimate the user's emotions and adjust the input method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Step 2: The analysis unit analyzes the information input by the reception unit and calculates an efficient travel route. For example, the analysis unit proposes an optimal route taking into consideration traffic conditions, weather, the user's schedule, etc. The analysis unit can, for example, propose a route that avoids traffic congestion or a means of transportation that takes into account the weather. Step 3: The navigation unit displays the route calculated by the analysis unit and provides navigation. For example, the navigation unit provides navigation according to arrows and guide displays displayed on the AR glasses. The navigation unit can also provide information on the departure time of the next train and information on facilities around the destination in real time while traveling.

[0056] (Example 2) A business meeting navigation system according to an embodiment of the present invention allows users to travel efficiently in a new city. The system includes a reception unit for inputting details of a destination and a meeting; an analysis unit for analyzing the information input by the reception unit and calculating an efficient travel route; and a navigation unit for displaying the route calculated by the analysis unit and providing navigation. For example, when a user inputs details of a destination and a meeting, the LLM analyzes the input information and calculates the optimal travel route. The calculated route is displayed on the AR glasses, allowing the user to receive visual navigation. This allows users to travel efficiently in a new city without getting lost. Furthermore, the AR glasses can provide real-time information while traveling. For example, the glasses can display the next train departure time and information about facilities near the destination. This allows the business meeting navigation system to support users in traveling efficiently in a new city. For example, it can provide support for arriving at an important meeting on time. This is particularly effective for business professionals, as it can save time and reduce stress.

[0057] A business meeting navigation system according to an embodiment includes a reception unit, an analysis unit, and a navigation unit. The reception unit allows a user to input details of a destination and a meeting. For example, the reception unit allows a user to input information such as the location and start time of the meeting and participant information. The reception unit can accept, for example, text input or voice input. The reception unit can also estimate a user's emotions and adjust the input method based on the estimated user emotions. For example, if a user is feeling stressed, a simple interface can be provided to minimize the input steps. The analysis unit analyzes the information input by the reception unit and calculates an efficient travel route. For example, the analysis unit can propose an optimal route taking into account traffic conditions, weather, the user's schedule, and the like. The analysis unit can, for example, propose a route to avoid traffic congestion or a means of transportation appropriate for the weather. The navigation unit displays the route calculated by the analysis unit and provides navigation. For example, the navigation unit provides navigation by following arrows and guide displays displayed on the AR glasses. The navigation unit can also provide, for example, real-time information on the departure time of the next train and information on facilities around the destination while traveling. As a result, the business meeting navigation system according to the embodiment can support the user in efficiently traveling in a new city.

[0058] The reception unit can estimate the user's emotions and adjust the input method for destinations and meeting details based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input destinations and meeting details. This improves input efficiency by providing an input method that corresponds 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0059] The reception unit can analyze the user's past input history and suggest an efficient input method. For example, the reception unit automatically displays destinations or meeting details that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations or meeting details to be used during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0060] The reception unit can use voice recognition and gesture recognition to assist input when entering details of a destination or meeting. For example, the reception unit can automatically set the destination when the user simply inputs "location of next meeting" by voice. The reception unit can also allow the user to easily set the details of the destination or meeting by performing a specific gesture on the screen of the AR glasses. Furthermore, the reception unit can also allow the user to combine voice input and gesture input to more intuitively set the details of the destination or meeting. This improves input efficiency by using voice recognition and gesture recognition.

[0061] The reception unit can automatically acquire the user's current location information when entering details of a destination or meeting, simplifying the input process. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The reception unit can also suggest optimal candidate locations taking into account the distance from the user's current location when the user enters a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the user's current location in real time and reflect it as the departure point. This automatically acquires the user's current location information, eliminating the need for input.

[0062] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This improves input efficiency by providing an interface that corresponds 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0063] The reception unit can automatically suggest candidate locations by referring to the user's past travel history when inputting details of a destination or meeting. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest optimal candidate locations. This improves input efficiency by suggesting candidate locations based on the user's past travel history.

[0064] When inputting details of a destination or meeting, the reception unit can refer to the user's calendar information and make suggestions based on the schedule. For example, the reception unit can refer to the schedule registered in the user's calendar and automatically set the destination and meeting details. The reception unit can also suggest locations related to specific events as candidate locations based on the user's calendar information. Furthermore, the reception unit can also suggest the optimal route based on the schedule based on the user's calendar information. This improves input efficiency by making suggestions based on calendar information.

[0065] The reception unit can analyze the user's social media activity and suggest related places when inputting details of a destination or meeting. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. This improves the efficiency of input by suggesting related places based on social media activity.

[0066] The analysis unit can estimate the user's emotions and adjust route selection criteria based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can prioritize suggesting scenic routes. Furthermore, if the user is in a hurry, the analysis unit can prioritize suggesting the shortest route. Furthermore, if the user is feeling stressed, the analysis unit can prioritize suggesting routes that avoid crowds. This improves travel efficiency by providing routes that correspond 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 can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The analysis unit can calculate an efficient travel route by taking into account traffic conditions, weather, the user's schedule, and the user's health condition. For example, if the user is tired, the analysis unit can suggest the shortest route. Also, if the user is seeking healthy exercise, the analysis unit can suggest a slightly longer route. Furthermore, if the user is feeling unwell, the analysis unit can suggest a route that includes rest points. This improves travel efficiency by providing a route that takes into account the user's health condition.

[0068] During analysis, the analysis unit can propose hybrid routes that combine multiple means of transportation. For example, the analysis unit can propose a route that combines trains and buses. The analysis unit can also propose a route that combines taxis and bicycles. Furthermore, the analysis unit can also propose a route that combines trains and walking. This improves travel efficiency by combining multiple means of transportation.

[0069] During analysis, the analysis unit can refer to the user's past travel history and prioritize suggesting a preferred route. The analysis unit can, for example, suggest an optimal route based on routes the user has used in the past. The analysis unit can also suggest a route that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most efficient route. This improves travel efficiency by suggesting a preferred route based on the user's past travel history.

[0070] The analysis unit can estimate the user's emotions and adjust the route display method based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This improves travel efficiency 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 generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] During analysis, the analysis unit can propose a route taking into consideration the user's geographical preferences. For example, the analysis unit can prioritize scenic routes. The analysis unit can also prioritize quiet routes. Furthermore, the analysis unit can propose an optimal route based on the user's past selection history. This improves travel efficiency by providing a route that takes into consideration the user's geographical preferences.

[0072] During the analysis, the analysis unit can take into consideration the user's eco-consciousness and prioritize suggesting environmentally friendly means of transportation. For example, the analysis unit can prioritize public transportation. The analysis unit can also prioritize bicycles and walking. Furthermore, the analysis unit can also suggest routes that combine environmentally friendly means of transportation. This improves travel efficiency by providing environmentally friendly means of transportation.

[0073] The analysis unit can continuously improve the route calculation algorithm by reflecting user feedback during analysis. For example, the analysis unit improves the route calculation algorithm based on user feedback. The analysis unit can also analyze the user's past feedback and propose an optimal route. Furthermore, the analysis unit can reflect user feedback in real time and improve the route calculation algorithm. In this way, by reflecting feedback, the accuracy of route calculation is improved.

[0074] The navigation unit can estimate the user's emotions and adjust the navigation guidance method based on the estimated user emotions. For example, if the user is nervous, the navigation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the navigation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can also provide a display method that focuses on the main points. This improves travel efficiency by providing a guidance method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The navigation unit can adjust the timing of guidance during navigation according to the user's walking speed and means of transportation. For example, if the user changes their walking speed, the navigation unit adjusts the navigation progress speed in real time. The navigation unit can also adjust the timing of navigation guidance in real time if the user changes their means of transportation. Furthermore, the navigation unit can pause navigation when the user stops and resume it when the user starts walking again. This improves the efficiency of travel by providing guidance timing according to the user's walking speed and means of transportation.

[0076] The navigation unit can track the user's gaze during navigation and optimize the guidance display according to the direction of the gaze. For example, when the user directs their gaze in a specific direction, the navigation unit can emphasize the guidance display related to that direction. Furthermore, when the user moves their gaze, the navigation unit can also update the guidance display in real time in accordance with the movement of the gaze. Furthermore, when the user fixes their gaze, the navigation unit can display detailed information related to the direction of the gaze. This improves the efficiency of travel by providing guidance display according to the direction of the gaze.

[0077] The navigation unit can update the user's current location information in real time during navigation and recalculate the optimal route. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the user's current location in real time and suggest the optimal route as the user approaches the destination. Furthermore, if the user gets lost, the navigation unit can update the user's current location in real time and perform navigation again. This allows the optimal route to be provided by updating the current location information in real time.

[0078] The navigation unit can estimate the user's emotions and adjust the navigation voice guidance based on the estimated user's emotions. For example, if the user is nervous, the navigation unit can provide guidance in a calm voice. Also, if the user is relaxed, the navigation unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the navigation unit can provide quick and concise voice guidance. This improves travel efficiency by providing voice guidance according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The navigation unit can provide guidance in cooperation with the user's device information during navigation. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Also, if the user is using a smartwatch, the navigation unit can provide a simple and highly visible display method. Furthermore, if the user is using a tablet, the navigation unit can provide a display method that is optimized for a large screen. This allows optimal guidance to be provided by coordinating with the device information.

[0080] The navigation unit can customize the theme and color of the guidance display according to the user's preferences during navigation. For example, the navigation unit customizes the guidance display by selecting a theme with a color scheme that the user prefers. The navigation unit can also customize the guidance display by selecting a font style that the user prefers. Furthermore, the navigation unit can suggest optimal themes and colors based on the user's past selection history. This improves the efficiency of travel by providing guidance displays that meet the user's preferences.

[0081] During navigation, the navigation unit can refer to the user's past travel history and prioritize displaying frequently used routes. For example, the navigation unit can prioritize displaying routes that the user has frequently used in the past. The navigation unit can also prioritize displaying routes that avoid congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and prioritize displaying the most efficient route. This improves travel efficiency by providing optimal routes based on the user's past travel history. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, and navigation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input details of the destination and the meeting. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the input information and calculating an efficient travel route. The navigation unit is implemented, for example, by the output device 40 of the smart device 14, displaying the calculated route and providing navigation. For example, the route is displayed on the display 40A of the smart device 14, allowing the user to visually receive navigation. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, and navigation unit, described above, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input details of a destination or meeting. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the input information and calculating an efficient travel route. The navigation unit is implemented, for example, by the speaker 240 of the smart glasses 214, displaying the calculated route and providing navigation. For example, the route is displayed on the display of the smart glasses 214, allowing the user to visually receive navigation. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and navigation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and allows the user to input details of a destination or meeting. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information and calculates an efficient travel route. The navigation unit is realized, for example, by the display 343 of the headset terminal 314, and displays the calculated route and provides navigation. For example, the route is displayed on the display of the headset terminal 314, allowing the user to visually receive navigation. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, and navigation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input details of the destination and the meeting. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the input information and calculating an efficient travel route. The navigation unit is realized, for example, by the speaker 240 of the robot 414, displaying the calculated route and providing navigation. For example, the route is displayed on the display of the robot 414, allowing the user to receive visual navigation.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The reception unit can also monitor the user's health condition and adjust the input method. For example, if the user is tired, voice input can be prioritized to reduce the burden of manual input. Also, if the user is in good health, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in poor health, the reception unit can provide a simple interface to enable quick completion of input. This improves input efficiency by providing input methods according to the user's health condition.

[0084] The reception unit can estimate the user's emotions and adjust the confirmation method for the input content based on the estimated user's emotions. For example, if the user is nervous, the confirmation screen can be simplified to minimize the confirmation steps. Alternatively, if the user is relaxed, detailed confirmation options can be provided to carefully confirm the input content. Furthermore, if the user is in a hurry, voice confirmation can be prioritized to allow the confirmation to be completed quickly. This improves input efficiency by providing confirmation methods that correspond to the user's emotions.

[0085] The analysis unit can analyze the user's past travel history and suggest rest points during travel. For example, it can suggest cafes or restaurants that the user has frequently visited in the past as rest points. Also, if the user tends to take rests at specific times of the day, it can suggest rest points that match those times. Furthermore, it can predict and suggest optimal rest points based on the user's past travel history. This improves travel efficiency by suggesting rest points based on the user's past travel history.

[0086] The analysis unit can estimate the user's emotions and suggest transportation methods based on the estimated user emotions. For example, if the user is relaxed, healthy transportation methods such as walking or cycling can be suggested. If the user is in a hurry, quick transportation methods such as taxis or trains can be suggested. Furthermore, if the user is feeling stressed, transportation methods that avoid crowds can be suggested. This improves the efficiency of travel by providing transportation methods that correspond to the user's emotions.

[0087] The analysis unit can adjust the amount of exercise during travel taking into account the user's health condition. For example, if the user is seeking healthy exercise, it can suggest a route that includes walking or cycling. Also, if the user is tired, it can suggest the shortest route to minimize the amount of exercise. Furthermore, if the user is feeling unwell, it can suggest a route that includes rest points. This improves travel efficiency by providing a route that takes into account the user's health condition.

[0088] The navigation unit can estimate the user's emotions and adjust the navigation voice guidance based on the estimated user's emotions. For example, if the user is nervous, the navigation unit can provide guidance in a calm voice. If the user is relaxed, the navigation unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the navigation unit can provide quick and concise voice guidance. This improves the efficiency of travel by providing voice guidance that matches the user's emotions.

[0089] The navigation unit can refer to the user's past travel history and prioritize the display of frequently used routes. For example, it can prioritize the display of routes that the user has used frequently in the past. It can also prioritize the display of routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and prioritize the display of the most efficient route. This improves travel efficiency by providing optimal routes based on the user's past travel history.

[0090] The navigation unit can estimate the user's emotions and adjust the navigation guidance method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the efficiency of travel can be improved by providing a guidance method that corresponds to the user's emotions.

[0091] The navigation unit can provide guidance in cooperation with the user's device information. For example, if the user is using a smartphone, a display method suited to the screen size can be provided. Also, if the user is using a smartwatch, a simple and highly visible display method can be provided. Furthermore, if the user is using a tablet, a display method optimized for a large screen can be provided. In this way, by coordinating with the device information, optimal guidance can be provided.

[0092] The navigation unit can estimate the user's emotions and adjust the navigation guidance display based on the estimated user's emotions. For example, if the user is nervous, a display with calm colors can be provided to reduce visual stress. If the user is having fun, a display with bright colors can be provided to make navigation more enjoyable. Furthermore, if the user is tired, a simple, highly visible display can be provided to make navigation easier. In this way, by providing a display that corresponds to the user's emotions, the efficiency of travel can be improved.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception unit allows the user to input details of the destination and meeting. For example, the reception unit allows the user to input the location and start time of the meeting, participant information, etc. The reception unit can accept, for example, text input or voice input. The reception unit can also estimate the user's emotions and adjust the input method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Step 2: The analysis unit analyzes the information input by the reception unit and calculates an efficient travel route. For example, the analysis unit proposes an optimal route taking into consideration traffic conditions, weather, the user's schedule, etc. The analysis unit can, for example, propose a route that avoids traffic congestion or a means of transportation that takes into account the weather. Step 3: The navigation unit displays the route calculated by the analysis unit and provides navigation. For example, the navigation unit provides navigation according to arrows and guide displays displayed on the AR glasses. The navigation unit can also provide information on the departure time of the next train and information on facilities around the destination in real time while traveling.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where you can enter your destination and meeting details, an analysis unit that analyzes the information input by the reception unit and calculates an efficient travel route; a navigation unit that displays the route calculated by the analysis unit and provides navigation. A system characterized by:

2. The reception unit Inferring user sentiment and adjusting how destination and meeting details are entered based on the inferred user sentiment 2. The system of claim 1.

3. The reception unit Analyzes the user's input history and suggests efficient input methods 2. The system of claim 1.

4. The reception unit Uses voice and gesture recognition to assist with input of destination and meeting details 2. The system of claim 1.

5. The reception unit Simplify input by automatically capturing your current location when entering destination or meeting details 2. The system of claim 1.

6. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering destination or meeting details, the app will automatically suggest possible locations based on the user's past travel history.

2. The system of claim 1.

8. The reception unit Look up your calendar information to make schedule-based suggestions as you enter destination and meeting details 2. The system of claim 1.

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