system

Abstract

The system according to the embodiment aims to provide comprehensive support to long-distance drivers, from optimizing their driving routes to providing health management, mental support, a conversation partner, and economical meal suggestions. [Solution] A system according to an embodiment includes an optimization unit, a health management unit, a mental support unit, a conversation unit, and a meal suggestion unit. The optimization unit optimizes the travel route. The health management unit manages health based on the travel route optimized by the optimization unit. The mental support unit provides mental support based on health data managed by the health management unit. The conversation unit engages in conversation based on the support provided by the mental support unit. The meal suggestion unit suggests cost-effective meals based on conversation data provided by the conversation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of lacking a system that comprehensively supports long-distance drivers by optimizing their routes, managing their health, and providing mental support.

[0005] The system according to the embodiment aims to provide comprehensive support to long-distance drivers, from optimizing their driving routes to providing health management, mental support, a conversation partner, and economical meal suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes an optimization unit, a health management unit, a mental support unit, a conversation unit, and a meal suggestion unit. The optimization unit optimizes the travel route. The health management unit manages health based on the travel route optimized by the optimization unit. The mental support unit provides mental support based on health data managed by the health management unit. The conversation unit engages in conversation based on the support provided by the mental support unit. The meal suggestion unit suggests cost-effective meals based on the conversation data provided by the conversation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide comprehensive support for long-distance drivers, from optimizing their driving routes to providing health management, mental support, a conversation partner, and economical meal suggestions. [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 support system according to an embodiment of the present invention uses AI to provide comprehensive support to meet the diverse needs of long-distance drivers. This support system offers support such as route optimization, health management, mental support, conversation partners, and economical meal recommendations. The support system combines real-time traffic information, company operation data, and external data such as weather and accident information to suggest cost-effective routes. At the same time, it incorporates the driver's health data to suggest appropriate health management and rest periods. Furthermore, the support system grows through conversations with drivers and provides optimal support for each individual driver. This avoids problems caused by missed reports and omissions and improves quality of life. For example, a user requests route optimization through an app. The support system then combines real-time traffic information, company operation data, and external data such as weather and accident information to suggest the optimal route. For example, it calculates the most efficient route taking traffic congestion and weather information into account. The support system then incorporates the driver's health data and suggests appropriate health management and rest periods. For example, it analyzes the driver's heart rate and sleep data to suggest appropriate rest periods. This helps maintain the driver's health and reduces the risk of accidents. Furthermore, the support system grows through conversations with drivers, providing optimal support for each individual driver. For example, it conducts conversations tailored to the driver's preferences and personality, providing mental support. This reduces stress for drivers and improves work efficiency. The support system also makes economical meal suggestions. For example, it suggests cost-effective dining options based on the driver's current location and preferences. This improves the driver's diet and keeps them healthy. In this way, comprehensive support using AI can improve the quality of life for long-distance drivers and increase efficiency in the logistics industry. This allows the support system to comprehensively support long-distance drivers by optimizing their driving routes, managing their health, providing mental support, providing a conversation partner, and suggesting economical meals.

[0029] The support system according to the embodiment includes an optimization unit, a health management unit, a mental support unit, a conversation unit, and a meal suggestion unit. The optimization unit optimizes the driving route by combining, for example, real-time traffic information, company operation data, and external data such as weather and accident information. For example, the optimization unit calculates the most efficient route taking into account traffic congestion information and weather information. The optimization unit can also propose an optimal route based on the company operation data, taking into account operation schedules and vehicle location information. The health management unit performs health management based on the driving route optimized by the optimization unit. For example, the health management unit incorporates the driver's health data and suggests appropriate health management and rest periods. For example, the health management unit analyzes the driver's heart rate and sleep data and suggests appropriate rest periods. The health management unit can also monitor the driver's health status in real time and issue an alert if an abnormality is detected. The mental support unit provides mental support based on the health data managed by the health management unit. For example, the mental support unit grows through conversations with the driver and provides optimal mental support for each individual driver. For example, the mental support unit provides mental support by holding a conversation tailored to the driver's preferences and personality. The mental support unit can also monitor the driver's stress level and suggest relaxation methods as needed. The conversation unit engages in conversation based on the support provided by the mental support unit. The conversation unit provides mental support by holding a conversation tailored to the driver's preferences and personality, for example. For example, the conversation unit selects topics and holds conversations based on the driver's interests and concerns. The conversation unit can also monitor the driver's emotional state and provide appropriate feedback. The meal suggestion unit makes cost-effective meal suggestions based on the conversation data provided by the conversation unit. The meal suggestion unit suggests cost-effective dining locations based on the driver's current location and preferences, for example. For example, the meal suggestion unit suggests dining locations close to the driver's current location and provides healthy meals.The meal suggestion unit can also analyze the driver's meal history and suggest meals that suit their preferences. As a result, the support system according to the embodiment can comprehensively support long-distance drivers in optimizing their driving routes, managing their health, providing mental support, acting as a conversation partner, and suggesting economical meals.

[0030] The optimization unit can optimize a route by combining real-time traffic information, company operation data, and external data such as weather and accident information. The optimization unit, for example, acquires real-time traffic information and uses it to optimize the route. For example, the optimization unit acquires traffic congestion information and accident information in real time and calculates an optimal route. The optimization unit can also propose an optimal route based on the company's operation data, taking into account operation schedules and vehicle location information. For example, the optimization unit analyzes the company's operation data and selects an optimal route based on the operation schedule. The optimization unit can also optimize a route by taking into account weather information. For example, the optimization unit proposes a route that is less affected by weather based on weather forecasts and weather warnings. This enables more accurate route optimization by combining external data. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without AI. For example, the optimization unit can optimize a route using an AI model that inputs real-time traffic information, company operation data, weather, and accident information and outputs an optimal route.

[0031] The health management unit can input the driver's health data and make recommendations for health management and rest periods. The health management unit can input, for example, the driver's health data, such as heart rate, blood pressure, and sleep data, and make recommendations for appropriate health management and rest periods. For example, the health management unit can monitor the driver's heart rate and issue an alert if an abnormality is detected. The health management unit can also analyze the driver's sleep data and suggest appropriate rest periods. For example, the health management unit can suggest rest periods and rest locations based on the driver's sleep data. The health management unit can also monitor the driver's health status in real time and issue an alert if an abnormality is detected. This makes it possible to suggest appropriate health management and rest periods based on the driver's health data. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without AI. For example, the health management unit can make recommendations for health management and rest periods using an AI model that inputs the driver's health data and outputs recommendations for health management and rest periods.

[0032] The mental support unit can learn through conversations with drivers and provide mental support to individual drivers. For example, the mental support unit grows through conversations with drivers and provides optimal mental support for individual drivers. For example, the mental support unit provides mental support by holding conversations tailored to the driver's preferences and personality. The mental support unit can also monitor the driver's stress level and suggest relaxation methods as needed. For example, the mental support unit can suggest relaxation methods based on the driver's stress level. The mental support unit can also monitor the driver's emotional state and provide appropriate feedback. This allows the mental support unit to grow through conversations with drivers and provide optimal mental support for individual drivers. Some or all of the above-described processing in the mental support unit may be performed, for example, using AI, or may be performed without using AI. For example, the mental support unit can provide mental support using an AI model that inputs conversation data with the driver and outputs mental support.

[0033] The conversation unit can provide mental support by conducting conversations tailored to the driver's preferences and personality. The conversation unit can provide mental support by conducting conversations tailored to the driver's preferences and personality, for example. For example, the conversation unit can select a topic and conduct a conversation based on the driver's interests. The conversation unit can also monitor the driver's emotional state and provide appropriate feedback. For example, the conversation unit can suggest words of encouragement or relaxation techniques based on the driver's emotional state. The conversation unit can also analyze the driver's past conversation history and select optimal conversation content. For example, the conversation unit can suggest topics of interest based on the driver's past conversation history. This improves the effectiveness of mental support by conducting conversations tailored to the driver's preferences and personality. Some or all of the above-described processing in the conversation unit can be performed using, for example, AI, or without AI. For example, the conversation unit can conduct a conversation using an AI model that inputs the driver's preferences and personality data and outputs the conversation content.

[0034] The meal suggestion unit can suggest cost-effective dining locations based on the driver's current location and preferences. The meal suggestion unit can, for example, suggest optimal dining locations based on the driver's current location. For example, the meal suggestion unit can suggest dining locations close to the driver's current location and provide healthy meals. The meal suggestion unit can also suggest cost-effective dining locations based on the driver's preferences. For example, the meal suggestion unit can suggest optimal dining locations based on the driver's preferences and past dining history. The meal suggestion unit can also analyze the driver's dining history and suggest healthy meals. For example, the meal suggestion unit can suggest nutritious meals based on the driver's dining history. This allows cost-effective dining locations to be suggested based on the driver's current location and preferences. Some or all of the above-described processing in the meal suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the meal suggestion unit can make meal suggestions using an AI model that inputs the driver's current location and preference data and outputs dining locations.

[0035] When optimizing a travel route, the optimization unit can analyze the driver's past travel history and select a route. The optimization unit, for example, proposes an optimal route based on the driver's past travel history. For example, the optimization unit selects an optimal route based on routes the driver has used in the past. The optimization unit can also propose a route that avoids congestion based on the driver's past travel history. For example, the optimization unit analyzes the driver's past travel history and proposes the most efficient route. This makes it possible to select an optimal route based on the driver's past travel history. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize a travel route using an AI model that inputs the driver's past travel history data and outputs an optimal route.

[0036] When optimizing a route, the optimization unit can select an optimal route based on the driver's current driving status and the vehicle's condition. For example, the optimization unit acquires the driver's current driving status in real time and proposes an optimal route. For example, the optimization unit selects an optimal route based on the driver's current driving status. The optimization unit can also propose an optimal route taking into account the vehicle's remaining fuel level and maintenance status. For example, the optimization unit proposes a route including a refueling point if refueling is necessary based on the vehicle's remaining fuel level. The optimization unit can also propose a route including a rest point based on the driver's driving status. For example, the optimization unit proposes appropriate rest points based on the driver's driving status. This makes it possible to propose an optimal route taking into account the driver's current driving status and the vehicle's condition. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without AI. For example, the optimization unit can optimize a route using an AI model that inputs the driver's current driving status and vehicle condition data and outputs an optimal route.

[0037] When optimizing a travel route, the optimization unit can prioritize suggesting a highly relevant route by taking into account the driver's geographical location information. The optimization unit, for example, prioritizes suggesting a route that is closest to the driver's current location. For example, the optimization unit selects an optimal route based on the driver's geographical location information. The optimization unit can also prioritize suggesting a route that avoids congestion based on the driver's geographical location information. For example, the optimization unit proposes the most efficient route based on the driver's geographical location information. This allows for prioritized suggestion of a highly relevant route by taking into account the driver's geographical location information. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize a travel route by using an AI model that inputs the driver's geographical location information data and outputs an optimal route.

[0038] The optimization unit can analyze the driver's social media activity and suggest a relevant route when optimizing the route. For example, the optimization unit can analyze the driver's social media posts and suggest a route that passes through places of interest. For example, the optimization unit can suggest an optimal route based on the driver's social media check-in history. The optimization unit can also suggest a preferred route based on the driver's social media activity. For example, the optimization unit can suggest a route that passes through places of interest based on the driver's social media posts. This makes it possible to suggest a relevant route based on the driver's social media activity. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize the route using an AI model that inputs the driver's social media activity data and outputs an optimal route.

[0039] During health management, the health management unit can analyze the driver's past health data and select a health management method. The health management unit, for example, proposes an optimal exercise plan based on the driver's past health data. For example, the health management unit proposes an optimal exercise plan based on the driver's past health data. The health management unit can also propose an appropriate meal plan based on the driver's past health data. For example, the health management unit proposes an appropriate meal plan based on the driver's past health data. The health management unit can also analyze the driver's past health data and propose optimal rest periods. For example, the health management unit proposes optimal rest periods based on the driver's past health data. This allows the optimal health management method to be selected based on the driver's past health data. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without using AI. For example, the health management unit can select a health management method using an AI model that inputs the driver's past health data and outputs an optimal health management method.

[0040] The health management unit can perform selection based on the driver's current living situation and driving situation during health management. The health management unit, for example, proposes an optimal health management method taking into account the driver's current living situation. For example, the health management unit proposes an optimal health management method based on the driver's current living situation. The health management unit can also propose appropriate rest times based on the driver's driving situation. For example, the health management unit proposes appropriate rest times based on the driver's driving situation. The health management unit can also propose an optimal meal plan based on the driver's current living situation and driving situation. For example, the health management unit proposes an optimal meal plan based on the driver's current living situation and driving situation. This makes it possible to propose an optimal health management method taking into account the driver's current living situation and driving situation. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without using AI. For example, the health management unit can select a health management method using an AI model that inputs data on the driver's current living situation and driving situation and outputs an optimal health management method.

[0041] During health management, the health management unit can prioritize suggesting highly relevant health management methods by taking into account the driver's geographical location information. The health management unit, for example, suggests exercise facilities close to the driver's current location. For example, the health management unit suggests the most appropriate exercise facility based on the driver's geographical location information. The health management unit can also suggest the most appropriate rest area based on the driver's geographical location information. For example, the health management unit suggests the most appropriate rest area based on the driver's geographical location information. The health management unit can also suggest healthy eating spots by taking into account the driver's geographical location information. For example, the health management unit suggests healthy eating spots based on the driver's geographical location information. This allows highly relevant health management methods to be prioritized by taking into account the driver's geographical location information. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without using AI. For example, the health management unit can suggest a health management method using an AI model that inputs the driver's geographical location information and outputs the most appropriate health management method.

[0042] When providing mental support, the mental support unit can analyze the driver's past mental state and select a support method. The mental support unit, for example, proposes an optimal support method based on the driver's past mental state. For example, the mental support unit proposes an optimal support method based on the driver's past mental state. The mental support unit can also propose a method to reduce stress based on the driver's past mental state. For example, the mental support unit proposes a method to reduce stress based on the driver's past mental state. The mental support unit can also analyze the driver's past mental state and propose an optimal relaxation method. For example, the mental support unit proposes an optimal relaxation method based on the driver's past mental state. This makes it possible to select an optimal support method based on the driver's past mental state. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can select a mental support method using an AI model that inputs data on the driver's past mental state and outputs an optimal support method.

[0043] When providing mental support, the mental support unit can select a method based on the driver's current living situation and driving situation. The mental support unit, for example, considers the driver's current living situation to propose an optimal mental support method. For example, the mental support unit proposes an optimal mental support method based on the driver's current living situation. The mental support unit can also propose an appropriate relaxation method based on the driver's driving situation. For example, the mental support unit proposes an appropriate relaxation method based on the driver's driving situation. The mental support unit can also propose an optimal stress reduction method based on the driver's current living situation and driving situation. For example, the mental support unit proposes an optimal stress reduction method based on the driver's current living situation and driving situation. This makes it possible to propose an optimal mental support method taking the driver's current living situation and driving situation into consideration. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can select a mental support method using an AI model that inputs data on the driver's current living situation and driving situation and outputs an optimal mental support method.

[0044] When providing mental support, the mental support unit can prioritize suggesting highly relevant support methods by taking into account the driver's geographical location information. The mental support unit, for example, suggests relaxation facilities close to the driver's current location. For example, the mental support unit suggests the most appropriate relaxation facility based on the driver's geographical location information. The mental support unit can also suggest the most appropriate rest location based on the driver's geographical location information. For example, the mental support unit suggests the most appropriate rest location based on the driver's geographical location information. The mental support unit can also suggest places that are useful for reducing stress by taking into account the driver's geographical location information. For example, the mental support unit suggests places that are useful for reducing stress based on the driver's geographical location information. This makes it possible to prioritize suggesting highly relevant support methods by taking into account the driver's geographical location information. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can suggest a mental support method using an AI model that inputs the driver's geographical location information data and outputs the most appropriate support method.

[0045] The conversation unit can analyze the driver's past conversation history during a conversation and select the conversation content. The conversation unit, for example, suggests an optimal topic based on the driver's past conversation history. For example, the conversation unit suggests an optimal topic based on the driver's past conversation history. The conversation unit can also suggest a topic of interest based on the driver's past conversation history. For example, the conversation unit suggests a topic of interest based on the driver's past conversation history. The conversation unit can also analyze the driver's past conversation history and suggest the optimal conversation content. For example, the conversation unit suggests the optimal conversation content based on the driver's past conversation history. This makes it possible to select the optimal conversation content based on the driver's past conversation history. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can select the conversation content using an AI model that inputs the driver's past conversation history data and outputs the optimal conversation content.

[0046] The conversation unit can select conversation content based on the driver's current living situation and driving situation during the conversation. The conversation unit, for example, considers the driver's current living situation to suggest optimal conversation content. For example, the conversation unit proposes optimal conversation content based on the driver's current living situation. The conversation unit can also suggest appropriate topics based on the driver's driving situation. For example, the conversation unit proposes appropriate topics based on the driver's driving situation. The conversation unit can also suggest optimal conversation content based on the driver's current living situation and driving situation. For example, the conversation unit proposes optimal conversation content based on the driver's current living situation and driving situation. This makes it possible to suggest optimal conversation content taking into consideration the driver's current living situation and driving situation. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can select conversation content using an AI model that inputs data on the driver's current living situation and driving situation and outputs optimal conversation content.

[0047] During a conversation, the conversation unit can prioritize suggesting highly relevant conversation content by taking into account the driver's geographical location information. The conversation unit, for example, provides topics related to the driver's current location. For example, the conversation unit provides local news and event information based on the driver's geographical location information. The conversation unit can also provide topics about local tourist spots and famous places based on the driver's geographical location information. For example, the conversation unit provides topics about local tourist spots and famous places based on the driver's geographical location information. This allows highly relevant conversation content to be prioritized by taking into account the driver's geographical location information. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can suggest conversation content using an AI model that inputs the driver's geographical location information data and outputs optimal conversation content.

[0048] When suggesting a meal, the meal suggestion unit can analyze the driver's past meal history and select a suggestion method. The meal suggestion unit, for example, can suggest an optimal dining location based on the driver's past meal history. For example, the meal suggestion unit can suggest an optimal dining location based on the driver's past meal history. The meal suggestion unit can also suggest meals that suit the driver's preferences based on the driver's past meal history. For example, the meal suggestion unit can suggest meals that suit the driver's preferences based on the driver's past meal history. The meal suggestion unit can also analyze the driver's past meal history and suggest healthy meals. For example, the meal suggestion unit can suggest healthy meals based on the driver's past meal history. This makes it possible to suggest an optimal dining location based on the driver's past meal history. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can select a meal suggestion method using an AI model that inputs the driver's past meal history data and outputs optimal dining locations.

[0049] When suggesting a meal, the meal suggestion unit can select the meal based on the driver's current living situation and driving situation. The meal suggestion unit, for example, can suggest an optimal place to eat, taking into account the driver's current living situation. For example, the meal suggestion unit can suggest an optimal place to eat based on the driver's current living situation. The meal suggestion unit can also suggest an appropriate meal time based on the driver's driving situation. For example, the meal suggestion unit can suggest an appropriate meal time based on the driver's driving situation. The meal suggestion unit can also suggest a healthy meal based on the driver's current living situation and driving situation. For example, the meal suggestion unit can suggest a healthy meal based on the driver's current living situation and driving situation. This makes it possible to suggest an optimal place to eat, taking into account the driver's current living situation and driving situation. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can select a method for suggesting a meal using an AI model that inputs data on the driver's current living situation and driving situation and outputs optimal places to eat.

[0050] When suggesting a meal, the meal suggestion unit can prioritize suggesting highly relevant eating locations by taking into account the driver's geographical location information. The meal suggestion unit, for example, prioritizes suggesting eating locations close to the driver's current location. For example, the meal suggestion unit can suggest eating locations that serve local specialty dishes based on the driver's geographical location information. The meal suggestion unit can also prioritize suggesting healthy eating locations based on the driver's geographical location information. For example, the meal suggestion unit prioritizes suggesting healthy eating locations based on the driver's geographical location information. This allows highly relevant eating locations to be prioritized by taking into account the driver's geographical location information. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can suggest a method of suggesting meals using an AI model that inputs the driver's geographical location information and outputs optimal eating locations.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The support system can further include a fuel economy management unit. The fuel economy management unit can provide advice to optimize fuel economy based on the driver's driving style and the vehicle's condition. For example, the fuel economy management unit can notify the driver to avoid sudden acceleration and braking. The fuel economy management unit can also monitor the vehicle's maintenance status and suggest necessary maintenance. Furthermore, the fuel economy management unit can suggest optimal refueling points and support cost-efficient driving. This can improve fuel economy and reduce operating costs.

[0053] The support system can further include a safe driving support unit. The safe driving support unit can monitor the driver's driving behavior in real time and provide feedback to promote safe driving. For example, the safe driving support unit can detect lane departure or speeding and issue a warning to the driver. The safe driving support unit can also monitor the driver's fatigue level and suggest appropriate breaks. Furthermore, the safe driving support unit can analyze past driving data and provide advice to improve the driver's driving skills. This reduces the risk of accidents and supports safe driving.

[0054] The support system can further include an emergency response unit. The emergency response unit can provide support to quickly respond when the driver faces an emergency. For example, the emergency response unit can automatically call the nearest rescue service in the event of an accident or breakdown. The emergency response unit can also notify emergency contacts in the event of a sudden change in the driver's health condition. Furthermore, the emergency response unit can track the driver's location in real time and provide information to quickly respond in the event of an emergency. This ensures the driver's safety and enables a quick response to emergencies.

[0055] The support system may further include an energy management unit. The energy management unit can provide advice to optimize the vehicle's energy consumption. For example, the energy management unit may monitor the remaining battery level of an electric vehicle and suggest the best charging point. The energy management unit may also analyze the vehicle's energy consumption patterns and suggest efficient driving methods. Furthermore, the energy management unit may provide information to promote the use of renewable energy. This makes it possible to optimize energy consumption and reduce the environmental impact.

[0056] The support system may further include an environmental information providing unit. The environmental information providing unit may provide the driver with environmental information during driving. For example, the environmental information providing unit may provide real-time information on air pollution and noise levels along the driving route. The environmental information providing unit may also provide advice to the driver to help them drive in an environmentally friendly manner. Furthermore, the environmental information providing unit may visualize the impact of the driver's driving behavior on the environment and promote the reduction of environmental load. This allows the driver to drive in an environmentally friendly manner and supports sustainable driving.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The optimization unit optimizes the route. The optimization unit optimizes the route by combining real-time traffic information, company operation data, and external data such as weather and accident information. For example, it calculates the most efficient route taking into account traffic congestion and weather information. It also proposes the optimal route based on company operation data, taking into account operation schedules and vehicle location information. Step 2: The health management unit performs health management based on the route optimized by the optimization unit. The health management unit collects the driver's health data and proposes appropriate health management and rest periods. For example, it analyzes heart rate and sleep data to propose appropriate rest periods. It also monitors the driver's health status in real time and issues an alert if an abnormality is detected. Step 3: The Mental Support Department provides mental support based on the health data managed by the Health Management Department. The Mental Support Department grows through conversations with drivers and provides optimal mental support for each individual driver. For example, it conducts conversations tailored to the driver's preferences and personality, monitors stress levels, and suggests relaxation methods as needed. Step 4: The conversation unit engages in conversation based on the support provided by the mental support unit. The conversation unit provides mental support by conducting conversations tailored to the driver's preferences and personality. For example, it selects topics based on the driver's interests and concerns, monitors their emotional state, and provides appropriate feedback. Step 5: The meal suggestion unit makes cost-effective meal suggestions based on the conversation data provided by the conversation unit. The meal suggestion unit suggests cost-effective dining locations based on the driver's current location and preferences. For example, it suggests dining locations close to the current location and offers healthy meals. It also analyzes the driver's eating history and suggests meals that suit the driver's preferences.

[0059] (Example 2) A support system according to an embodiment of the present invention uses AI to provide comprehensive support to meet the diverse needs of long-distance drivers. This support system offers support such as route optimization, health management, mental support, conversation partners, and economical meal recommendations. The support system combines real-time traffic information, company operation data, and external data such as weather and accident information to suggest cost-effective routes. At the same time, it incorporates the driver's health data to suggest appropriate health management and rest periods. Furthermore, the support system grows through conversations with drivers and provides optimal support for each individual driver. This avoids problems caused by missed reports and omissions and improves quality of life. For example, a user requests route optimization through an app. The support system then combines real-time traffic information, company operation data, and external data such as weather and accident information to suggest the optimal route. For example, it calculates the most efficient route taking traffic congestion and weather information into account. The support system then incorporates the driver's health data and suggests appropriate health management and rest periods. For example, it analyzes the driver's heart rate and sleep data to suggest appropriate rest periods. This helps maintain the driver's health and reduces the risk of accidents. Furthermore, the support system grows through conversations with drivers, providing optimal support for each individual driver. For example, it conducts conversations tailored to the driver's preferences and personality, providing mental support. This reduces stress for drivers and improves work efficiency. The support system also makes economical meal suggestions. For example, it suggests cost-effective dining options based on the driver's current location and preferences. This improves the driver's diet and keeps them healthy. In this way, comprehensive support using AI can improve the quality of life for long-distance drivers and increase efficiency in the logistics industry. This allows the support system to comprehensively support long-distance drivers by optimizing their driving routes, managing their health, providing mental support, providing a conversation partner, and suggesting economical meals.

[0060] The support system according to the embodiment includes an optimization unit, a health management unit, a mental support unit, a conversation unit, and a meal suggestion unit. The optimization unit optimizes the driving route by combining, for example, real-time traffic information, company operation data, and external data such as weather and accident information. For example, the optimization unit calculates the most efficient route taking into account traffic congestion information and weather information. The optimization unit can also propose an optimal route based on the company operation data, taking into account operation schedules and vehicle location information. The health management unit performs health management based on the driving route optimized by the optimization unit. For example, the health management unit incorporates the driver's health data and suggests appropriate health management and rest periods. For example, the health management unit analyzes the driver's heart rate and sleep data and suggests appropriate rest periods. The health management unit can also monitor the driver's health status in real time and issue an alert if an abnormality is detected. The mental support unit provides mental support based on the health data managed by the health management unit. For example, the mental support unit grows through conversations with the driver and provides optimal mental support for each individual driver. For example, the mental support unit provides mental support by holding a conversation tailored to the driver's preferences and personality. The mental support unit can also monitor the driver's stress level and suggest relaxation methods as needed. The conversation unit engages in conversation based on the support provided by the mental support unit. The conversation unit provides mental support by holding a conversation tailored to the driver's preferences and personality, for example. For example, the conversation unit selects topics and holds conversations based on the driver's interests and concerns. The conversation unit can also monitor the driver's emotional state and provide appropriate feedback. The meal suggestion unit makes cost-effective meal suggestions based on the conversation data provided by the conversation unit. The meal suggestion unit suggests cost-effective dining locations based on the driver's current location and preferences, for example. For example, the meal suggestion unit suggests dining locations close to the driver's current location and provides healthy meals.The meal suggestion unit can also analyze the driver's meal history and suggest meals that suit their preferences. As a result, the support system according to the embodiment can comprehensively support long-distance drivers in optimizing their driving routes, managing their health, providing mental support, acting as a conversation partner, and suggesting economical meals.

[0061] The optimization unit can optimize a route by combining real-time traffic information, company operation data, and external data such as weather and accident information. The optimization unit, for example, acquires real-time traffic information and uses it to optimize the route. For example, the optimization unit acquires traffic congestion information and accident information in real time and calculates an optimal route. The optimization unit can also propose an optimal route based on the company's operation data, taking into account operation schedules and vehicle location information. For example, the optimization unit analyzes the company's operation data and selects an optimal route based on the operation schedule. The optimization unit can also optimize a route by taking into account weather information. For example, the optimization unit proposes a route that is less affected by weather based on weather forecasts and weather warnings. This enables more accurate route optimization by combining external data. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without AI. For example, the optimization unit can optimize a route using an AI model that inputs real-time traffic information, company operation data, weather, and accident information and outputs an optimal route.

[0062] The health management unit can input the driver's health data and make recommendations for health management and rest periods. The health management unit can input, for example, the driver's health data, such as heart rate, blood pressure, and sleep data, and make recommendations for appropriate health management and rest periods. For example, the health management unit can monitor the driver's heart rate and issue an alert if an abnormality is detected. The health management unit can also analyze the driver's sleep data and suggest appropriate rest periods. For example, the health management unit can suggest rest periods and rest locations based on the driver's sleep data. The health management unit can also monitor the driver's health status in real time and issue an alert if an abnormality is detected. This makes it possible to suggest appropriate health management and rest periods based on the driver's health data. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without AI. For example, the health management unit can make recommendations for health management and rest periods using an AI model that inputs the driver's health data and outputs recommendations for health management and rest periods.

[0063] The mental support unit can learn through conversations with drivers and provide mental support to individual drivers. For example, the mental support unit grows through conversations with drivers and provides optimal mental support for individual drivers. For example, the mental support unit provides mental support by holding conversations tailored to the driver's preferences and personality. The mental support unit can also monitor the driver's stress level and suggest relaxation methods as needed. For example, the mental support unit can suggest relaxation methods based on the driver's stress level. The mental support unit can also monitor the driver's emotional state and provide appropriate feedback. This allows the mental support unit to grow through conversations with drivers and provide optimal mental support for individual drivers. Some or all of the above-described processing in the mental support unit may be performed, for example, using AI, or may be performed without using AI. For example, the mental support unit can provide mental support using an AI model that inputs conversation data with the driver and outputs mental support.

[0064] The conversation unit can provide mental support by conducting conversations tailored to the driver's preferences and personality. The conversation unit can provide mental support by conducting conversations tailored to the driver's preferences and personality, for example. For example, the conversation unit can select a topic and conduct a conversation based on the driver's interests. The conversation unit can also monitor the driver's emotional state and provide appropriate feedback. For example, the conversation unit can suggest words of encouragement or relaxation techniques based on the driver's emotional state. The conversation unit can also analyze the driver's past conversation history and select optimal conversation content. For example, the conversation unit can suggest topics of interest based on the driver's past conversation history. This improves the effectiveness of mental support by conducting conversations tailored to the driver's preferences and personality. Some or all of the above-described processing in the conversation unit can be performed using, for example, AI, or without AI. For example, the conversation unit can conduct a conversation using an AI model that inputs the driver's preferences and personality data and outputs the conversation content.

[0065] The meal suggestion unit can suggest cost-effective dining locations based on the driver's current location and preferences. The meal suggestion unit can, for example, suggest optimal dining locations based on the driver's current location. For example, the meal suggestion unit can suggest dining locations close to the driver's current location and provide healthy meals. The meal suggestion unit can also suggest cost-effective dining locations based on the driver's preferences. For example, the meal suggestion unit can suggest optimal dining locations based on the driver's preferences and past dining history. The meal suggestion unit can also analyze the driver's dining history and suggest healthy meals. For example, the meal suggestion unit can suggest nutritious meals based on the driver's dining history. This allows cost-effective dining locations to be suggested based on the driver's current location and preferences. Some or all of the above-described processing in the meal suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the meal suggestion unit can make meal suggestions using an AI model that inputs the driver's current location and preference data and outputs dining locations.

[0066] The optimization unit can estimate the user's emotions and adjust the proposed route based on the estimated user emotions. For example, the optimization unit can estimate the user's emotions and adjust the proposed route based on the estimated emotions. For example, if the user is feeling stressed, the optimization unit can suggest a scenic route that allows the user to relax. Furthermore, if the user is in a hurry, the optimization unit can suggest a route that can reach the destination in the shortest time. For example, if the user is tired, the optimization unit can suggest a route with many rest stops. In this way, by adjusting the proposed route according to the user's emotions, a more appropriate route can be provided. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can adjust the proposed route using an AI model that inputs user emotion data and outputs a route.

[0067] When optimizing a travel route, the optimization unit can analyze the driver's past travel history and select a route. The optimization unit, for example, proposes an optimal route based on the driver's past travel history. For example, the optimization unit selects an optimal route based on routes the driver has used in the past. The optimization unit can also propose a route that avoids congestion based on the driver's past travel history. For example, the optimization unit analyzes the driver's past travel history and proposes the most efficient route. This makes it possible to select an optimal route based on the driver's past travel history. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize a travel route using an AI model that inputs the driver's past travel history data and outputs an optimal route.

[0068] When optimizing a route, the optimization unit can select an optimal route based on the driver's current driving status and the vehicle's condition. For example, the optimization unit acquires the driver's current driving status in real time and proposes an optimal route. For example, the optimization unit selects an optimal route based on the driver's current driving status. The optimization unit can also propose an optimal route taking into account the vehicle's remaining fuel level and maintenance status. For example, the optimization unit proposes a route including a refueling point if refueling is necessary based on the vehicle's remaining fuel level. The optimization unit can also propose a route including a rest point based on the driver's driving status. For example, the optimization unit proposes appropriate rest points based on the driver's driving status. This makes it possible to propose an optimal route taking into account the driver's current driving status and the vehicle's condition. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without AI. For example, the optimization unit can optimize a route using an AI model that inputs the driver's current driving status and vehicle condition data and outputs an optimal route.

[0069] The optimization unit can estimate the user's emotions and determine the priority of route sequences based on the estimated user emotions. The optimization unit, for example, estimates the user's emotions and determines the priority of route sequences based on the estimated emotions. For example, if the user is relaxed, the optimization unit prioritizes scenic routes. Furthermore, if the user is in a hurry, the optimization unit can prioritize routes that can reach the destination in the shortest time. For example, if the user is tired, the optimization unit prioritizes routes with many rest stops. This allows the priority of route sequences to be determined according to the user's emotions, thereby providing a more appropriate route. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without AI. For example, the optimization unit can determine the priority of route sequences using an AI model that inputs user emotion data and outputs route priorities.

[0070] When optimizing a travel route, the optimization unit can prioritize suggesting a highly relevant route by taking into account the driver's geographical location information. The optimization unit, for example, prioritizes suggesting a route that is closest to the driver's current location. For example, the optimization unit selects an optimal route based on the driver's geographical location information. The optimization unit can also prioritize suggesting a route that avoids congestion based on the driver's geographical location information. For example, the optimization unit proposes the most efficient route based on the driver's geographical location information. This allows for prioritized suggestion of a highly relevant route by taking into account the driver's geographical location information. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize a travel route by using an AI model that inputs the driver's geographical location information data and outputs an optimal route.

[0071] The optimization unit can analyze the driver's social media activity and suggest a relevant route when optimizing the route. For example, the optimization unit can analyze the driver's social media posts and suggest a route that passes through places of interest. For example, the optimization unit can suggest an optimal route based on the driver's social media check-in history. The optimization unit can also suggest a preferred route based on the driver's social media activity. For example, the optimization unit can suggest a route that passes through places of interest based on the driver's social media posts. This makes it possible to suggest a relevant route based on the driver's social media activity. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize the route using an AI model that inputs the driver's social media activity data and outputs an optimal route.

[0072] The health management unit can estimate the user's emotions and adjust the health management method based on the estimated user emotions. The health management unit, for example, estimates the user's emotions and adjusts the health management method based on the estimated emotions. For example, if the user is feeling stressed, the health management unit can suggest relaxing exercises and rest methods. Furthermore, if the user is relaxed, the health management unit can also suggest exercises to maintain health. For example, if the user is tired, the health management unit can suggest appropriate rest times and methods. This enables more appropriate health management by adjusting the health management method according to the user's emotions. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without using AI. For example, the health management unit can adjust the health management method using an AI model that inputs the user's emotion data and outputs a health management method.

[0073] During health management, the health management unit can analyze the driver's past health data and select a health management method. The health management unit, for example, proposes an optimal exercise plan based on the driver's past health data. For example, the health management unit proposes an optimal exercise plan based on the driver's past health data. The health management unit can also propose an appropriate meal plan based on the driver's past health data. For example, the health management unit proposes an appropriate meal plan based on the driver's past health data. The health management unit can also analyze the driver's past health data and propose optimal rest periods. For example, the health management unit proposes optimal rest periods based on the driver's past health data. This allows the optimal health management method to be selected based on the driver's past health data. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without using AI. For example, the health management unit can select a health management method using an AI model that inputs the driver's past health data and outputs an optimal health management method.

[0074] The health management unit can perform selection based on the driver's current living situation and driving situation during health management. The health management unit, for example, proposes an optimal health management method taking into account the driver's current living situation. For example, the health management unit proposes an optimal health management method based on the driver's current living situation. The health management unit can also propose appropriate rest times based on the driver's driving situation. For example, the health management unit proposes appropriate rest times based on the driver's driving situation. The health management unit can also propose an optimal meal plan based on the driver's current living situation and driving situation. For example, the health management unit proposes an optimal meal plan based on the driver's current living situation and driving situation. This makes it possible to propose an optimal health management method taking into account the driver's current living situation and driving situation. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without using AI. For example, the health management unit can select a health management method using an AI model that inputs data on the driver's current living situation and driving situation and outputs an optimal health management method.

[0075] The health management unit can estimate the user's emotions and determine health management priorities based on the estimated user emotions. The health management unit, for example, estimates the user's emotions and determines health management priorities based on the estimated emotions. For example, if the user is feeling stressed, the health management unit can prioritize suggesting ways to relax. Furthermore, if the user is relaxed, the health management unit can prioritize suggesting exercises to maintain health. For example, if the user is tired, the health management unit can prioritize suggesting appropriate rest periods. This enables more appropriate health management by determining health management priorities based on the user's emotions. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without AI. For example, the health management unit can determine health management priorities using an AI model that inputs user emotion data and outputs health management priorities.

[0076] During health management, the health management unit can prioritize suggesting highly relevant health management methods by taking into account the driver's geographical location information. The health management unit, for example, suggests exercise facilities close to the driver's current location. For example, the health management unit suggests the most appropriate exercise facility based on the driver's geographical location information. The health management unit can also suggest the most appropriate rest area based on the driver's geographical location information. For example, the health management unit suggests the most appropriate rest area based on the driver's geographical location information. The health management unit can also suggest healthy eating spots by taking into account the driver's geographical location information. For example, the health management unit suggests healthy eating spots based on the driver's geographical location information. This allows highly relevant health management methods to be prioritized by taking into account the driver's geographical location information. Some or all of the above-described processing in the health management unit may be performed using, for example, AI, or may be performed without using AI. For example, the health management unit can suggest a health management method using an AI model that inputs the driver's geographical location information and outputs the most appropriate health management method.

[0077] The mental support unit can estimate the user's emotions and adjust the mental support method based on the estimated user emotions. For example, the mental support unit can estimate the user's emotions and adjust the mental support method based on the estimated emotions. For example, if the user is feeling stressed, the mental support unit can provide relaxing conversation. Furthermore, if the user is relaxed, the mental support unit can also provide pleasant topics. For example, if the user is tired, the mental support unit can provide encouraging words. This allows for more appropriate mental support by adjusting the mental support method according to the user's emotions. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can adjust the mental support method using an AI model that inputs the user's emotion data and outputs a mental support method.

[0078] When providing mental support, the mental support unit can analyze the driver's past mental state and select a support method. The mental support unit, for example, proposes an optimal support method based on the driver's past mental state. For example, the mental support unit proposes an optimal support method based on the driver's past mental state. The mental support unit can also propose a method to reduce stress based on the driver's past mental state. For example, the mental support unit proposes a method to reduce stress based on the driver's past mental state. The mental support unit can also analyze the driver's past mental state and propose an optimal relaxation method. For example, the mental support unit proposes an optimal relaxation method based on the driver's past mental state. This makes it possible to select an optimal support method based on the driver's past mental state. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can select a mental support method using an AI model that inputs data on the driver's past mental state and outputs an optimal support method.

[0079] When providing mental support, the mental support unit can select a method based on the driver's current living situation and driving situation. The mental support unit, for example, considers the driver's current living situation to propose an optimal mental support method. For example, the mental support unit proposes an optimal mental support method based on the driver's current living situation. The mental support unit can also propose an appropriate relaxation method based on the driver's driving situation. For example, the mental support unit proposes an appropriate relaxation method based on the driver's driving situation. The mental support unit can also propose an optimal stress reduction method based on the driver's current living situation and driving situation. For example, the mental support unit proposes an optimal stress reduction method based on the driver's current living situation and driving situation. This makes it possible to propose an optimal mental support method taking the driver's current living situation and driving situation into consideration. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can select a mental support method using an AI model that inputs data on the driver's current living situation and driving situation and outputs an optimal mental support method.

[0080] The mental support unit can estimate the user's emotions and determine the priority of mental support based on the estimated user emotions. The mental support unit, for example, estimates the user's emotions and determines the priority of mental support based on the estimated emotions. For example, if the user is feeling stressed, the mental support unit can prioritize suggesting ways to relax. The mental support unit can also prioritize providing pleasant topics when the user is relaxed. For example, if the user is tired, the mental support unit can prioritize providing words of encouragement. This enables more appropriate mental support by determining the priority of mental support according to the user's emotions. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can determine the priority of mental support using an AI model that inputs user emotion data and outputs the priority of mental support.

[0081] When providing mental support, the mental support unit can prioritize suggesting highly relevant support methods by taking into account the driver's geographical location information. The mental support unit, for example, suggests relaxation facilities close to the driver's current location. For example, the mental support unit suggests the most appropriate relaxation facility based on the driver's geographical location information. The mental support unit can also suggest the most appropriate rest location based on the driver's geographical location information. For example, the mental support unit suggests the most appropriate rest location based on the driver's geographical location information. The mental support unit can also suggest places that are useful for reducing stress by taking into account the driver's geographical location information. For example, the mental support unit suggests places that are useful for reducing stress based on the driver's geographical location information. This makes it possible to prioritize suggesting highly relevant support methods by taking into account the driver's geographical location information. Some or all of the above-described processing in the mental support unit may be performed using, for example, AI, or may be performed without using AI. For example, the mental support unit can suggest a mental support method using an AI model that inputs the driver's geographical location information data and outputs the most appropriate support method.

[0082] The conversation unit can estimate the user's emotions and adjust the content of the conversation based on the estimated user emotions. The conversation unit, for example, estimates the user's emotions and adjusts the content of the conversation based on the estimated emotions. For example, if the user is feeling stressed, the conversation unit can provide a relaxing topic. The conversation unit can also provide an enjoyable topic if the user is relaxed. For example, if the user is tired, the conversation unit can provide words of encouragement. This allows for more appropriate conversation by adjusting the content of the conversation according to the user's emotions. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can adjust the content of the conversation using an AI model that inputs user emotion data and outputs conversation content.

[0083] The conversation unit can analyze the driver's past conversation history during a conversation and select the conversation content. The conversation unit, for example, suggests an optimal topic based on the driver's past conversation history. For example, the conversation unit suggests an optimal topic based on the driver's past conversation history. The conversation unit can also suggest a topic of interest based on the driver's past conversation history. For example, the conversation unit suggests a topic of interest based on the driver's past conversation history. The conversation unit can also analyze the driver's past conversation history and suggest the optimal conversation content. For example, the conversation unit suggests the optimal conversation content based on the driver's past conversation history. This makes it possible to select the optimal conversation content based on the driver's past conversation history. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can select the conversation content using an AI model that inputs the driver's past conversation history data and outputs the optimal conversation content.

[0084] The conversation unit can select conversation content based on the driver's current living situation and driving situation during the conversation. The conversation unit, for example, considers the driver's current living situation to suggest optimal conversation content. For example, the conversation unit proposes optimal conversation content based on the driver's current living situation. The conversation unit can also suggest appropriate topics based on the driver's driving situation. For example, the conversation unit proposes appropriate topics based on the driver's driving situation. The conversation unit can also suggest optimal conversation content based on the driver's current living situation and driving situation. For example, the conversation unit proposes optimal conversation content based on the driver's current living situation and driving situation. This makes it possible to suggest optimal conversation content taking into consideration the driver's current living situation and driving situation. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can select conversation content using an AI model that inputs data on the driver's current living situation and driving situation and outputs optimal conversation content.

[0085] The conversation unit can estimate the user's emotions and determine conversation priorities based on the estimated user emotions. The conversation unit, for example, estimates the user's emotions and determines conversation priorities based on the estimated emotions. For example, if the user is feeling stressed, the conversation unit can prioritize relaxing topics. Also, if the user is relaxed, the conversation unit can prioritize happy topics. For example, if the user is tired, the conversation unit can prioritize encouraging words. This enables more appropriate conversations by determining conversation priorities based on the user's emotions. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can determine conversation priorities using an AI model that inputs user emotion data and outputs conversation priorities.

[0086] During a conversation, the conversation unit can prioritize suggesting highly relevant conversation content by taking into account the driver's geographical location information. The conversation unit, for example, provides topics related to the driver's current location. For example, the conversation unit provides local news and event information based on the driver's geographical location information. The conversation unit can also provide topics about local tourist spots and famous places based on the driver's geographical location information. For example, the conversation unit provides topics about local tourist spots and famous places based on the driver's geographical location information. This allows highly relevant conversation content to be prioritized by taking into account the driver's geographical location information. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can suggest conversation content using an AI model that inputs the driver's geographical location information data and outputs optimal conversation content.

[0087] The meal suggestion unit can estimate the user's emotions and adjust the method of meal suggestion based on the estimated user's emotions. The meal suggestion unit, for example, can estimate the user's emotions and adjust the method of meal suggestion based on the estimated emotions. For example, if the user is feeling stressed, the meal suggestion unit can suggest a relaxing dining spot. The meal suggestion unit can also suggest healthy meals when the user is relaxed. For example, if the user is tired, the meal suggestion unit can suggest nutritious meals. This allows for more appropriate meal suggestions by adjusting the method of meal suggestion according to the user's emotions. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can adjust the method of meal suggestion using an AI model that inputs the user's emotional data and outputs a method of meal suggestion.

[0088] When suggesting a meal, the meal suggestion unit can analyze the driver's past meal history and select a suggestion method. The meal suggestion unit, for example, can suggest an optimal dining location based on the driver's past meal history. For example, the meal suggestion unit can suggest an optimal dining location based on the driver's past meal history. The meal suggestion unit can also suggest meals that suit the driver's preferences based on the driver's past meal history. For example, the meal suggestion unit can suggest meals that suit the driver's preferences based on the driver's past meal history. The meal suggestion unit can also analyze the driver's past meal history and suggest healthy meals. For example, the meal suggestion unit can suggest healthy meals based on the driver's past meal history. This makes it possible to suggest an optimal dining location based on the driver's past meal history. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can select a meal suggestion method using an AI model that inputs the driver's past meal history data and outputs optimal dining locations.

[0089] When suggesting a meal, the meal suggestion unit can select the meal based on the driver's current living situation and driving situation. The meal suggestion unit, for example, can suggest an optimal place to eat, taking into account the driver's current living situation. For example, the meal suggestion unit can suggest an optimal place to eat based on the driver's current living situation. The meal suggestion unit can also suggest an appropriate meal time based on the driver's driving situation. For example, the meal suggestion unit can suggest an appropriate meal time based on the driver's driving situation. The meal suggestion unit can also suggest a healthy meal based on the driver's current living situation and driving situation. For example, the meal suggestion unit can suggest a healthy meal based on the driver's current living situation and driving situation. This makes it possible to suggest an optimal place to eat, taking into account the driver's current living situation and driving situation. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can select a method for suggesting a meal using an AI model that inputs data on the driver's current living situation and driving situation and outputs optimal places to eat.

[0090] The meal suggestion unit can estimate the user's emotions and determine the priority of meal suggestions based on the estimated user emotions. The meal suggestion unit, for example, estimates the user's emotions and determines the priority of meal suggestions based on the estimated emotions. For example, if the user is feeling stressed, the meal suggestion unit can prioritize suggesting relaxing dining places. The meal suggestion unit can also prioritize suggesting healthy meals when the user is relaxed. For example, if the user is tired, the meal suggestion unit can prioritize suggesting nutritious meals. This enables more appropriate meal suggestions by determining the priority of meal suggestions according to the user's emotions. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can determine the priority of meal suggestions using an AI model that inputs user emotion data and outputs the priority of meal suggestions.

[0091] When suggesting a meal, the meal suggestion unit can prioritize suggesting highly relevant eating locations by taking into account the driver's geographical location information. The meal suggestion unit, for example, prioritizes suggesting eating locations close to the driver's current location. For example, the meal suggestion unit can suggest eating locations that serve local specialty dishes based on the driver's geographical location information. The meal suggestion unit can also prioritize suggesting healthy eating locations based on the driver's geographical location information. For example, the meal suggestion unit prioritizes suggesting healthy eating locations based on the driver's geographical location information. This allows highly relevant eating locations to be prioritized by taking into account the driver's geographical location information. Some or all of the above-described processing in the meal suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal suggestion unit can suggest a method of suggesting meals using an AI model that inputs the driver's geographical location information and outputs optimal eating locations. === Hard Collateral 1-1 === Each of the multiple elements, including the optimization unit, health management unit, mental support unit, conversation unit, and meal suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the optimization unit acquires real-time traffic and weather information via the control unit 46A of the smart device 14 and calculates an optimal driving route via the specific processing unit 290 of the data processing device 12. The health management unit acquires the driver's health data using sensors in the smart device 14 and analyzes the data via the specific processing unit 290 of the data processing device 12. The mental support unit converses with the driver via the control unit 46A of the smart device 14 and provides mental support via the specific processing unit 290 of the data processing device 12. The conversation unit converses with the driver using the microphone 38B and speaker 40B of the smart device 14 and provides appropriate feedback via the specific processing unit 290 of the data processing device 12. The meal suggestion unit identifies the driver's current location using location information from the smart device 14 and suggests cost-effective dining options via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the optimization unit, health management unit, mental support unit, conversation unit, and meal suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the optimization unit acquires real-time traffic and weather information via the control unit 46A of the smart glasses 214 and calculates an optimal driving route via the specific processing unit 290 of the data processing device 12. The health management unit acquires the driver's health data using sensors in the smart glasses 214 and analyzes the data via the specific processing unit 290 of the data processing device 12. The mental support unit converses with the driver via the control unit 46A of the smart glasses 214 and provides mental support via the specific processing unit 290 of the data processing device 12. The conversation unit converses with the driver using the microphone 238 and speaker 240 of the smart glasses 214 and provides appropriate feedback via the specific processing unit 290 of the data processing device 12. The meal suggestion unit identifies the driver's current location using location information from the smart glasses 214 and suggests cost-effective dining options via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the optimization unit, health management unit, mental support unit, conversation unit, and meal suggestion unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the optimization unit acquires real-time traffic information and weather information via the control unit 46A of the headset terminal 314, and calculates an optimal driving route via the specific processing unit 290 of the data processing device 12. The health management unit acquires health data of the driver using sensors in the headset terminal 314, and analyzes the data via the specific processing unit 290 of the data processing device 12. The mental support unit converses with the driver via the control unit 46A of the headset terminal 314, and provides mental support via the specific processing unit 290 of the data processing device 12. The conversation unit converses with the driver using the microphone 238 and speaker 240 of the headset terminal 314, and provides appropriate feedback via the specific processing unit 290 of the data processing device 12. The meal suggestion unit identifies the driver's current location using the location information of the headset terminal 314, and suggests cost-effective places to eat using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the optimization unit, health management unit, mental support unit, conversation unit, and meal suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the optimization unit acquires real-time traffic and weather information via the control unit 46A of the robot 414 and calculates an optimal driving route via the specific processing unit 290 of the data processing device 12. The health management unit acquires health data of the driver using sensors in the robot 414 and analyzes the data via the specific processing unit 290 of the data processing device 12. The mental support unit converses with the driver via the control unit 46A of the robot 414 and provides mental support via the specific processing unit 290 of the data processing device 12. The conversation unit converses with the driver using the microphone 238 and speaker 240 of the robot 414 and provides appropriate feedback via the specific processing unit 290 of the data processing device 12. The meal suggestion unit identifies the driver's current location using location information from the robot 414 and suggests cost-effective dining spots via the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The support system can further include an entertainment provider. The entertainment provider can provide appropriate entertainment content based on the driver's preferences and current emotional state. For example, if the driver wants to relax, it can suggest relaxing music or podcasts. If the driver is bored, it can also suggest interesting audiobooks or radio programs. Furthermore, the entertainment provider can analyze the driver's past viewing history and suggest new content that matches the driver's preferences. This can reduce the driver's stress while driving and support a more comfortable driving experience.

[0094] The support system can further include a fuel economy management unit. The fuel economy management unit can provide advice to optimize fuel economy based on the driver's driving style and the vehicle's condition. For example, the fuel economy management unit can notify the driver to avoid sudden acceleration and braking. The fuel economy management unit can also monitor the vehicle's maintenance status and suggest necessary maintenance. Furthermore, the fuel economy management unit can suggest optimal refueling points and support cost-efficient driving. This can improve fuel economy and reduce operating costs.

[0095] The support system can further include a safe driving support unit. The safe driving support unit can monitor the driver's driving behavior in real time and provide feedback to promote safe driving. For example, the safe driving support unit can detect lane departure or speeding and issue a warning to the driver. The safe driving support unit can also monitor the driver's fatigue level and suggest appropriate breaks. Furthermore, the safe driving support unit can analyze past driving data and provide advice to improve the driver's driving skills. This reduces the risk of accidents and supports safe driving.

[0096] The support system may further include a community collaboration unit, which may promote communication between drivers and support information sharing. For example, the community collaboration unit may provide a platform where drivers can share traffic information and rest stop information in real time. The community collaboration unit may also operate a forum where drivers can provide each other with advice and support. Furthermore, the community collaboration unit may plan community events to provide appropriate support, taking into account the emotional state of drivers. This may reduce drivers' feelings of loneliness and increase a sense of community unity.

[0097] The support system can further include an emergency response unit. The emergency response unit can provide support to quickly respond when the driver faces an emergency. For example, the emergency response unit can automatically call the nearest rescue service in the event of an accident or breakdown. The emergency response unit can also notify emergency contacts in the event of a sudden change in the driver's health condition. Furthermore, the emergency response unit can track the driver's location in real time and provide information to quickly respond in the event of an emergency. This ensures the driver's safety and enables a quick response to emergencies.

[0098] The support system may further include a feedback collection unit. The feedback collection unit may collect feedback from the driver and use the collected feedback to improve the system. For example, the feedback collection unit may provide an interface that allows the driver to report problems or areas for improvement that they have experienced while driving. The feedback collection unit may also conduct a survey to collect appropriate feedback, taking into account the driver's emotional state. Furthermore, the feedback collection unit may analyze the collected feedback and reflect it in improving the system. This makes it possible to improve the system in accordance with the driver's needs and provide better support.

[0099] The support system may further include an energy management unit. The energy management unit can provide advice to optimize the vehicle's energy consumption. For example, the energy management unit may monitor the remaining battery level of an electric vehicle and suggest the best charging point. The energy management unit may also analyze the vehicle's energy consumption patterns and suggest efficient driving methods. Furthermore, the energy management unit may provide information to promote the use of renewable energy. This makes it possible to optimize energy consumption and reduce the environmental impact.

[0100] The support system can further include a personalized training unit. The personalized training unit can provide training programs to improve the driver's driving skills. For example, the personalized training unit can analyze the driver's past driving data and propose the optimal training plan for each individual driver. The personalized training unit can also take the driver's emotional state into consideration and adjust the appropriate training content. Furthermore, the personalized training unit can monitor the progress of the training and provide feedback as necessary. This can support the driver in improving their driving skills and promoting safe driving.

[0101] The support system may further include an environmental information providing unit. The environmental information providing unit may provide the driver with environmental information during driving. For example, the environmental information providing unit may provide real-time information on air pollution and noise levels along the driving route. The environmental information providing unit may also provide advice to the driver to help them drive in an environmentally friendly manner. Furthermore, the environmental information providing unit may visualize the impact of the driver's driving behavior on the environment and promote the reduction of environmental load. This allows the driver to drive in an environmentally friendly manner and supports sustainable driving.

[0102] The support system may further include a reward provider. The reward provider may provide rewards according to the driver's driving behavior and health management results. For example, the reward provider may award points for safe driving and improved fuel efficiency, and the driver may use the points he or she has accumulated to receive special benefits. The reward provider may also provide health-related rewards according to the driver's health management results. Furthermore, the reward provider may provide rewards at appropriate times, taking into account the driver's emotional state. This may increase the driver's motivation and promote healthy and safe driving.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The optimization unit optimizes the route. The optimization unit optimizes the route by combining real-time traffic information, company operation data, and external data such as weather and accident information. For example, it calculates the most efficient route taking into account traffic congestion and weather information. It also proposes the optimal route based on company operation data, taking into account operation schedules and vehicle location information. Step 2: The health management unit performs health management based on the route optimized by the optimization unit. The health management unit collects the driver's health data and proposes appropriate health management and rest periods. For example, it analyzes heart rate and sleep data to propose appropriate rest periods. It also monitors the driver's health status in real time and issues an alert if an abnormality is detected. Step 3: The Mental Support Department provides mental support based on the health data managed by the Health Management Department. The Mental Support Department grows through conversations with drivers and provides optimal mental support for each individual driver. For example, it conducts conversations tailored to the driver's preferences and personality, monitors stress levels, and suggests relaxation methods as needed. Step 4: The conversation unit engages in conversation based on the support provided by the mental support unit. The conversation unit provides mental support by conducting conversations tailored to the driver's preferences and personality. For example, it selects topics based on the driver's interests and concerns, monitors their emotional state, and provides appropriate feedback. Step 5: The meal suggestion unit makes cost-effective meal suggestions based on the conversation data provided by the conversation unit. The meal suggestion unit suggests cost-effective dining locations based on the driver's current location and preferences. For example, it suggests dining locations close to the current location and offers healthy meals. It also analyzes the driver's eating history and suggests meals that suit the driver's preferences.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. an optimization unit that optimizes the route; a health management unit that performs health management based on the travel route optimized by the optimization unit; a mental support unit that provides mental support based on the health data managed by the health management unit; a conversation unit that engages in conversation based on the support provided by the mental support unit; a meal suggestion unit that suggests cost-effective meals based on the conversation data provided by the conversation unit; A system characterized by:

2. The optimization unit Optimize travel routes by combining real-time traffic information, company operation data, and external data on weather and accident information 2. The system of claim 1.

3. The health management department Collecting driver health data and providing health management and rest time suggestions 2. The system of claim 1.

4. The mental support department Learn through conversations with drivers and provide mental support to individual drivers 2. The system of claim 1.

5. The conversation unit is Conduct conversations tailored to the driver's preferences and personality, and provide mental support 2. The system of claim 1.

6. The meal suggestion unit Suggest cost-effective dining options based on the driver's location and preferences 2. The system of claim 1.

7. The optimization unit Estimate user emotions and adjust route suggestions based on the estimated user emotions 2. The system of claim 1.

8. The optimization unit When optimizing routes, the driver's past driving history is analyzed to select the route.

2. The system of claim 1.

9. The optimization unit When optimizing routes, drivers are selected based on their current driving situation and vehicle condition.

2. The system of claim 1.

10. The optimization unit Estimate the user's emotions and prioritize routes based on the estimated user emotions.

2. The system of claim 1.

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