System

Abstract

An object of a system according to an embodiment is to propose an optimal transportation plan by efficiently utilizing vacancy information of an express bus or a transportation truck.SOLUTION: A system includes an information collection unit, an analysis unit, a proposal unit, and a notification unit. The information collection unit collects vacancy information of an express bus or a transport truck. The analysis unit analyzes the vacancy information collected by the information collection unit. The proposal unit proposes an optimal transportation plan based on the information analyzed by the analysis unit. The notification unit notifies the user of the transportation plan proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to efficiently utilize information on available express buses and transport trucks, and there is room for improvement in reducing transportation costs and formulating efficient transportation plans.

[0005] The system according to the embodiment aims to efficiently utilize information on available express buses and transport trucks to propose optimal transport plans. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a notification unit. The information collection unit collects availability information for express buses or transport trucks. The analysis unit analyzes the availability information collected by the information collection unit. The proposal unit proposes an optimal transportation plan based on the information analyzed by the analysis unit. The notification unit notifies the user of the transportation plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently utilize information on available express buses and transport trucks to propose optimal transport plans. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The transportation matching system according to an embodiment of the present invention visualizes the availability of express buses and transport trucks online, and uses AI to propose optimal transportation plans. This enables the transportation matching system to provide convenient and economical transportation services to users.

[0029] A transportation matching system according to an embodiment includes an information collection unit, an analysis unit, a proposal unit, and a notification unit. The information collection unit collects vacancy information for express buses or transport trucks. For example, the information collection unit collects data such as the operation schedule, current number of passengers, and loading status of each bus or truck. The information collection unit can also collect operation information in real time and analyze information on available seats and trunk space. For example, the information collection unit uses GPS data to collect location information for buses and trucks and determine availability. The analysis unit analyzes the vacancy information collected by the information collection unit. For example, the analysis unit analyzes the availability of available seats and trunk space based on the collected data. The analysis unit can also analyze data using AI to generate an optimal transportation plan for a user. For example, the analysis unit uses a machine learning algorithm to predict availability based on past data. The proposal unit proposes an optimal transportation plan based on the information analyzed by the analysis unit. For example, the proposal unit generates an optimal transportation plan based on the user's departure point, destination, and desired transportation date and time. The proposal unit can also propose an optimal plan for a user using generation AI. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an optimal plan for the user. The notification unit notifies the user of the transportation plan proposed by the proposal unit. For example, the notification unit sends the user an email, SMS, or app notification. Furthermore, when the user agrees to the proposed plan, the notification unit can automatically confirm the reservation and send a confirmation notification. For example, the notification unit sends a reservation confirmation email after the user agrees to the plan. This allows the transportation matching system according to the embodiment to provide the user with an optimal transportation plan. For example, the user can check available seats and trunk room availability at a glance and select the optimal transportation method.

[0030] The information collection unit can analyze a user's past usage history and provide individually customized availability information. For example, the information collection unit uses AI to analyze a user's past usage history and understand the frequency of use during specific time periods or routes. For example, for a user who uses the service at the same time period every week, availability information for that time period is displayed preferentially. The information collection unit also analyzes a user's preferred seat position and trunk room usage status based on the user's past usage history and provides individually customized availability information. For example, for a user who prefers window seats, information about available window seats is displayed preferentially. The information collection unit also analyzes a user's past usage history and understands a preference for specific bus companies or truck transportation companies. For example, for a user who frequently uses a specific bus company, availability information from that company is displayed preferentially. This makes it possible to provide individually customized availability information to users.

[0031] The information collection unit can provide more accurate availability information based on at least one external data item, weather or traffic conditions. For example, the information collection unit uses AI to collect weather data and predict service conditions and availability information during bad weather. For example, if there is a possibility of service delays due to heavy rain or snow, the information collection unit provides availability information taking that information into account. Furthermore, the information collection unit uses AI to collect traffic condition data and provide availability information taking into account the effects of congestion and accidents. For example, the information collection unit prioritizes displaying availability information for buses and trucks on routes where congestion is expected. Furthermore, the information collection unit uses AI to integrate external data and update availability information in real time based on weather and traffic conditions. For example, if there is a sudden change in weather or a traffic accident, the information is immediately reflected in the availability information provided. This allows for accurate availability information that takes weather and traffic conditions into account.

[0032] The information collection unit can expand the scope of vacancy information collection to include not only express buses or transport trucks but also taxis or shared ride services, thereby providing comprehensive vacancy information for transportation means. For example, the information collection unit uses AI to collect vacancy information for taxis and shared ride services and provide comprehensive vacancy information for transportation means. For example, it displays taxi vacancies and available times for shared rides. The information collection unit also integrates vacancy information for express buses and transport trucks with vacancy information for taxis and shared ride services to suggest the optimal transportation means to users. For example, it suggests taxis or shared rides when a bus is fully booked. The information collection unit also updates vacancy information for taxis and shared ride services in real time to provide users with the latest information. For example, it immediately reflects taxi location information and shared ride reservation status. This makes it possible to provide comprehensive vacancy information for transportation means.

[0033] The information collection unit can also incorporate image or video data, allowing users to visually check availability. For example, the information collection unit uses AI to collect images and videos of the interior of buses and trucks, allowing users to visually check availability. For example, images of seat availability and trunk space are displayed. The information collection unit also uses image recognition technology to automatically analyze availability from the collected image and video data and provide the results to users. For example, image analysis can be used to determine seat availability and display availability information. The information collection unit also updates image and video data in real time so users can visually check availability. For example, live video of the interior of buses and trucks can be provided, allowing users to instantly check availability. This allows users to visually check availability.

[0034] The proposal unit can generate transportation plans that provide greater satisfaction by reflecting users' past ratings and feedback. For example, the proposal unit uses AI to analyze users' past ratings and feedback and generate transportation plans that provide greater satisfaction. For example, it prioritizes proposals of plans that have received high ratings in the past. The proposal unit also identifies areas for improvement in the transportation plan based on user feedback and reflects these in the next proposal. For example, it proposes a plan that improves on points of dissatisfaction in the past. The proposal unit also collects user rating data in real time and immediately reflects it, thereby always providing the latest plans that provide greater satisfaction. For example, if a user's rating is low, it immediately regenerates a plan. This makes it possible to provide transportation plans that increase user satisfaction.

[0035] The suggestion unit can select the optimal means of transportation based on the type or characteristics of the cargo. For example, the suggestion unit uses AI to analyze the type and characteristics of the cargo and select the optimal means of transportation. For example, for fragile cargo, the suggestion unit may suggest a truck equipped with shock-absorbing material. The suggestion unit may also suggest a refrigerated truck if refrigeration is required depending on the characteristics of the cargo. For example, refrigerated trucks may be selected as a priority for transporting fresh food or medicine. The suggestion unit may also select the optimal means of transportation in real time based on the type and characteristics of the cargo and suggest it to the user. For example, it may select an appropriate truck depending on the weight and size of the cargo. This makes it possible to provide the optimal means of transportation according to the type and characteristics of the cargo.

[0036] The proposal unit can expand the scope of its transportation plan proposals beyond personal cargo to corporate logistics, providing B2B transportation plans. For example, the proposal unit uses AI to analyze a company's logistics needs and propose B2B transportation plans. For example, it provides a plan for efficiently transporting large volumes of cargo. The proposal unit also generates optimal transportation plans based on the company's logistics data and proposes them to the company. For example, it proposes efficient transportation routes between multiple bases. The proposal unit also updates B2B transportation plans in real time and provides optimal plans tailored to the company's needs. For example, it proposes plans that can also accommodate sudden transportation requests. This makes it possible to provide transportation plans that also accommodate corporate logistics.

[0037] The suggestion unit can suggest related insurance and additional services based on the plan selected by the user. For example, the suggestion unit uses AI to suggest the optimal insurance plan based on the transportation plan selected by the user. For example, it suggests adding insurance to expensive luggage. The suggestion unit also suggests additional services related to the transportation plan. For example, it provides a packaging service for fragile luggage. The suggestion unit also suggests related services in real time based on the plan selected by the user. For example, it provides a luggage tracking service or a delivery notification service. This makes it possible to provide the user with related insurance and additional services.

[0038] When collecting information on empty trucks on the return journey, the information collection unit can propose eco-friendly transportation plans based on the fuel efficiency or environmental impact of the trucks. For example, the information collection unit uses AI to collect truck fuel efficiency data and propose transportation plans that prioritize the use of fuel-efficient trucks. For example, using fuel-efficient trucks reduces transportation costs. The information collection unit also considers the environmental impact and proposes transportation plans that use low-emission trucks. For example, using environmentally friendly trucks achieves eco-friendly transportation. The information collection unit also integrates truck fuel efficiency data and environmental impact data to propose optimal eco-friendly transportation plans in real time. For example, it provides a plan that takes into account the balance between fuel efficiency and environmental impact. This makes it possible to provide eco-friendly transportation plans.

[0039] When analyzing information on empty trucks on the way home, the information collection unit can propose an optimal schedule based on the truck driver's break times or working conditions. For example, the information collection unit uses AI to collect data on truck drivers' break times and propose an optimal schedule that takes break times into consideration. For example, it may provide a plan that includes appropriate breaks to reduce driver fatigue. The information collection unit may also consider working conditions and propose a schedule that complies with the driver's working hours. For example, it may provide a plan that ensures operation within statutory working hours. The information collection unit may also integrate the truck driver's break times and working conditions and propose an optimal schedule in real time. For example, it may provide a plan that takes into consideration the driver's health and safety. This makes it possible to provide an optimal schedule that takes into consideration the truck driver's break times and working conditions.

[0040] The information gathering unit can expand the scope of use of empty trucks on the return journey to include not only domestic but also international transport, thereby providing global transport plans. For example, the information gathering unit uses AI to collect information on empty trucks for international transport and propose global transport plans. For example, it can utilize international logistics networks to provide optimal transport routes. The information gathering unit also integrates data from domestic and international truck transport companies to propose plans that also support international transport. For example, it can provide intermodal transport plans from domestic to overseas. The information gathering unit also updates information on empty trucks for international transport in real time, providing users with the latest global transport plans. For example, it can provide plans that reflect international logistics trends. This makes it possible to provide global transport plans.

[0041] When collecting information on empty trucks on the way back, the information collection unit can track the truck's location information in real time and notify the user of the situation in real time. For example, the information collection unit uses AI to track the truck's location information in real time and notify the user of its current location and estimated arrival time. For example, it uses GPS data to display the truck's movement in real time. The information collection unit also builds a system that notifies the user of the situation in real time based on the truck's location information. For example, it sends a notification when the truck approaches its destination. The information collection unit also updates the truck's location information in real time to provide the user with the latest status. For example, it immediately notifies the user if the truck is delayed or its route is changed. This allows the truck's location information to be tracked in real time and the user to be provided with the latest status.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The information collection unit can also collect the user's health data and propose an optimal transportation plan based on the user's health condition. For example, if the user cannot tolerate long travel times, it can prioritize a route that will arrive in the shortest time. The information collection unit can also collect the user's allergy information and propose transportation methods that take allergies into consideration. For example, for a user who is allergic to pets, it can propose a bus that does not allow pets. The information collection unit can also propose a plan to provide a comfortable travel environment based on the user's health data. For example, for a user who suffers from back pain, it can propose a bus with reclining seats. This makes it possible to provide transportation plans that take the user's health condition into consideration.

[0044] The analysis unit can also provide tourist information and event information related to the transportation plan based on the user's hobbies and interests. For example, if the user is interested in music festivals, it can provide information about music events held in that area. The analysis unit can also suggest information about tourist spots and restaurants based on the user's hobbies. For example, if a user is a food lover, it can suggest popular restaurants near the destination. The analysis unit can also provide special offers and discount information related to the transportation plan based on the user's interests. For example, it can provide discount tickets to museums. This makes it possible to provide transportation plans with added value that match the user's hobbies and interests.

[0045] The suggestion unit can also take into account the user's social connections and suggest transportation plans that can be used with friends and family. For example, it can integrate availability information for friends and family heading to the same destination and suggest plans that allow them to travel together. The suggestion unit can also analyze the user's social media data and refer to transportation plans used by friends and family. For example, it can prioritize and suggest plans that have been highly rated by friends. The suggestion unit can also offer group discounts and special offers based on the user's social connections. For example, it can apply discounts when multiple people use the service. This makes it possible to provide transportation plans that take into account the user's social connections.

[0046] The information collection unit can expand the scope of vacancy information collection to include not only express buses or transport trucks but also taxis or shared ride services, thereby providing comprehensive vacancy information for transportation means. For example, AI can collect vacancy information for taxis and shared ride services and provide comprehensive vacancy information for transportation means. For example, it can display taxi vacancies and available times for shared rides. The information collection unit can also integrate vacancy information for express buses and transport trucks with vacancy information for taxis and shared ride services to suggest the optimal transportation means to users. For example, it can suggest taxis or shared rides when a bus is fully booked. The information collection unit can also update vacancy information for taxis and shared ride services in real time to provide users with the latest information. For example, it can instantly reflect taxi location information and shared ride reservation status. This makes it possible to provide comprehensive vacancy information for transportation means.

[0047] The information collection unit can also incorporate image or video data, allowing users to visually check availability. For example, AI can collect images and videos of the interior of buses and trucks, allowing users to visually check availability. For example, images can be displayed showing seat availability and trunk space. The information collection unit can also use image recognition technology to automatically analyze availability from the collected image and video data and provide the results to users. For example, image analysis can be used to determine seat availability and display availability information. The information collection unit can also update image and video data in real time, allowing users to visually check availability. For example, live video of the interior of buses and trucks can be provided, allowing users to instantly check availability. This allows users to visually check availability.

[0048] The proposal unit can generate transportation plans that provide higher satisfaction by reflecting the user's past ratings and feedback. For example, AI can analyze the user's past ratings and feedback to generate transportation plans that provide higher satisfaction. For example, it can prioritize proposals of plans that have received high ratings in the past. The proposal unit can also identify areas for improvement in the transportation plan based on the user's feedback and reflect these in the next proposal. For example, it can propose plans that improve on areas that users were dissatisfied with in the past. The proposal unit can also collect user rating data in real time and immediately reflect this data to always provide the latest plans that provide higher satisfaction. For example, if the user's rating is low, it will immediately regenerate a plan. This makes it possible to provide transportation plans that increase user satisfaction.

[0049] The suggestion unit can select the optimal means of transport based on the type or characteristics of the cargo. For example, AI analyzes the type and characteristics of the cargo and selects the optimal means of transport. For example, for fragile cargo, it may suggest a truck equipped with shock-absorbing materials. The suggestion unit may also suggest a refrigerated truck if refrigeration is required, depending on the characteristics of the cargo. For example, it may preferentially select a refrigerated truck for transporting fresh food or medicine. The suggestion unit may also select the optimal means of transport in real time based on the type and characteristics of the cargo and suggest it to the user. For example, it may select an appropriate truck depending on the weight and size of the cargo. This makes it possible to provide the optimal means of transport based on the type and characteristics of the cargo.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information collection unit collects information on vacant express buses or transport trucks. For example, the information collection unit collects data such as the operation schedule, current number of passengers, and loading status of each bus or truck. The information collection unit can also collect operation information in real time and analyze information on vacant seats and trunk space. For example, the information collection unit uses GPS data to collect location information of buses and trucks and determine vacant status. Step 2: The analysis unit analyzes the availability information collected by the information collection unit. For example, the analysis unit analyzes the status of available seats and trunk space based on the collected data. The analysis unit can also use AI to analyze the data and generate an optimal transportation plan for the user. For example, the analysis unit uses a machine learning algorithm to predict availability based on past data. Step 3: The proposal unit proposes an optimal transportation plan based on the information analyzed by the analysis unit. For example, the proposal unit generates an optimal transportation plan based on the user's departure point, destination, and desired transportation date and time. The proposal unit can also use a generation AI to propose an optimal plan to the user. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an optimal plan for the user. Step 4: The notification unit notifies the user of the transportation plan proposed by the proposal unit. For example, the notification unit sends the user an email, an SMS, or an app notification. The notification unit can also automatically confirm the reservation and send a confirmation notification when the user agrees to the proposed plan. For example, the notification unit sends a reservation confirmation email after the user agrees to the plan.

[0052] (Example 2) The transportation matching system according to an embodiment of the present invention visualizes the availability of express buses and transport trucks online, and uses AI to propose optimal transportation plans. This enables the transportation matching system to provide convenient and economical transportation services to users.

[0053] A transportation matching system according to an embodiment includes an information collection unit, an analysis unit, a proposal unit, and a notification unit. The information collection unit collects vacancy information for express buses or transport trucks. For example, the information collection unit collects data such as the operation schedule, current number of passengers, and loading status of each bus or truck. The information collection unit can also collect operation information in real time and analyze information on available seats and trunk space. For example, the information collection unit uses GPS data to collect location information for buses and trucks and determine availability. The analysis unit analyzes the vacancy information collected by the information collection unit. For example, the analysis unit analyzes the availability of available seats and trunk space based on the collected data. The analysis unit can also analyze data using AI to generate an optimal transportation plan for a user. For example, the analysis unit uses a machine learning algorithm to predict availability based on past data. The proposal unit proposes an optimal transportation plan based on the information analyzed by the analysis unit. For example, the proposal unit generates an optimal transportation plan based on the user's departure point, destination, and desired transportation date and time. The proposal unit can also propose an optimal plan for a user using generation AI. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an optimal plan for the user. The notification unit notifies the user of the transportation plan proposed by the proposal unit. For example, the notification unit sends the user an email, SMS, or app notification. Furthermore, when the user agrees to the proposed plan, the notification unit can automatically confirm the reservation and send a confirmation notification. For example, the notification unit sends a reservation confirmation email after the user agrees to the plan. This allows the transportation matching system according to the embodiment to provide the user with an optimal transportation plan. For example, the user can check available seats and trunk room availability at a glance and select the optimal transportation method.

[0054] The information collection unit can analyze a user's past usage history and provide individually customized availability information. For example, the information collection unit uses AI to analyze a user's past usage history and understand the frequency of use during specific time periods or routes. For example, for a user who uses the service at the same time period every week, availability information for that time period is displayed preferentially. The information collection unit also analyzes a user's preferred seat position and trunk room usage status based on the user's past usage history and provides individually customized availability information. For example, for a user who prefers window seats, information about available window seats is displayed preferentially. The information collection unit also analyzes a user's past usage history and understands a preference for specific bus companies or truck transportation companies. For example, for a user who frequently uses a specific bus company, availability information from that company is displayed preferentially. This makes it possible to provide individually customized availability information to users.

[0055] The information collection unit can provide more accurate availability information based on at least one external data item, weather or traffic conditions. For example, the information collection unit uses AI to collect weather data and predict service conditions and availability information during bad weather. For example, if there is a possibility of service delays due to heavy rain or snow, the information collection unit provides availability information taking that information into account. Furthermore, the information collection unit uses AI to collect traffic condition data and provide availability information taking into account the effects of congestion and accidents. For example, the information collection unit prioritizes displaying availability information for buses and trucks on routes where congestion is expected. Furthermore, the information collection unit uses AI to integrate external data and update availability information in real time based on weather and traffic conditions. For example, if there is a sudden change in weather or a traffic accident, the information is immediately reflected in the availability information provided. This allows for accurate availability information that takes weather and traffic conditions into account.

[0056] The information collection unit can use the emotion estimation function to analyze the emotions of a user when viewing availability information and optimize an information presentation method that elicits positive emotions. For example, the information collection unit uses the emotion estimation function to analyze facial expressions and voice when a user views availability information and calculates an emotion score. For example, the information collection unit preferentially adopts an information presentation method that elicits strong positive emotions. The information collection unit also monitors the user's emotional reactions in real time and optimizes an information presentation method that elicits positive emotions. For example, if the user smiles, that information presentation method is continued to be used. The information collection unit also learns information presentation methods that evoke positive emotions in the user based on the emotion estimation data and provides an optimal information presentation method. For example, information is presented that reflects the user's preferred colors and designs. This makes it possible to provide an information presentation method that elicits positive emotions in the user.

[0057] The information collection unit can expand the scope of vacancy information collection to include not only express buses or transport trucks but also taxis or shared ride services, thereby providing comprehensive vacancy information for transportation means. For example, the information collection unit uses AI to collect vacancy information for taxis and shared ride services and provide comprehensive vacancy information for transportation means. For example, it displays taxi vacancies and available times for shared rides. The information collection unit also integrates vacancy information for express buses and transport trucks with vacancy information for taxis and shared ride services to suggest the optimal transportation means to users. For example, it suggests taxis or shared rides when a bus is fully booked. The information collection unit also updates vacancy information for taxis and shared ride services in real time to provide users with the latest information. For example, it immediately reflects taxi location information and shared ride reservation status. This makes it possible to provide comprehensive vacancy information for transportation means.

[0058] The information collection unit can also incorporate image or video data, allowing users to visually check availability. For example, the information collection unit uses AI to collect images and videos of the interior of buses and trucks, allowing users to visually check availability. For example, images of seat availability and trunk space are displayed. The information collection unit also uses image recognition technology to automatically analyze availability from the collected image and video data and provide the results to users. For example, image analysis can be used to determine seat availability and display availability information. The information collection unit also updates image and video data in real time so users can visually check availability. For example, live video of the interior of buses and trucks can be provided, allowing users to instantly check availability. This allows users to visually check availability.

[0059] The information collecting unit can use the emotion estimation function to estimate the emotion of the user when entering availability information in real time, and provide an interface for reducing stress. For example, the information collecting unit uses the emotion estimation function to analyze the facial expression and voice of the user when entering availability information and calculate an emotion score. For example, if the user is feeling stressed, the interface is simplified. The information collecting unit also monitors the user's emotional reactions in real time and provides an interface for reducing stress. For example, if the user is confused, a help message is displayed. The information collecting unit also learns an interface that is less stressful for the user based on the emotion estimation data, and provides an optimal interface. For example, an interface that reflects the user's preferred colors and designs is provided. This makes it possible to provide an interface that reduces user stress.

[0060] The proposal unit can generate transportation plans that provide greater satisfaction by reflecting users' past ratings and feedback. For example, the proposal unit uses AI to analyze users' past ratings and feedback and generate transportation plans that provide greater satisfaction. For example, it prioritizes proposals of plans that have received high ratings in the past. The proposal unit also identifies areas for improvement in the transportation plan based on user feedback and reflects these in the next proposal. For example, it proposes a plan that improves on points of dissatisfaction in the past. The proposal unit also collects user rating data in real time and immediately reflects it, thereby always providing the latest plans that provide greater satisfaction. For example, if a user's rating is low, it immediately regenerates a plan. This makes it possible to provide transportation plans that increase user satisfaction.

[0061] The suggestion unit can select the optimal means of transportation based on the type or characteristics of the cargo. For example, the suggestion unit uses AI to analyze the type and characteristics of the cargo and select the optimal means of transportation. For example, for fragile cargo, the suggestion unit may suggest a truck equipped with shock-absorbing material. The suggestion unit may also suggest a refrigerated truck if refrigeration is required depending on the characteristics of the cargo. For example, refrigerated trucks may be selected as a priority for transporting fresh food or medicine. The suggestion unit may also select the optimal means of transportation in real time based on the type and characteristics of the cargo and suggest it to the user. For example, it may select an appropriate truck depending on the weight and size of the cargo. This makes it possible to provide the optimal means of transportation according to the type and characteristics of the cargo.

[0062] The suggestion unit can use the emotion estimation function to analyze the user's emotions regarding the proposed plan and preferentially suggest plans that elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to analyze the user's facial expressions and voice regarding the proposed plan and calculate an emotion score. For example, the suggestion unit preferentially suggests plans that evoke strong positive emotions. The suggestion unit also monitors the user's emotional reactions in real time and optimizes plans that elicit positive emotions. For example, if the user smiles, the suggestion unit preferentially suggests that plan. The suggestion unit also learns plans that evoke positive emotions in the user based on the emotion estimation data and provides optimal plans. For example, the suggestion unit proposes plans that reflect the user's preferred means of transportation and routes. This makes it possible to provide transportation plans that elicit positive emotions in the user.

[0063] The proposal unit can expand the scope of its transportation plan proposals beyond personal cargo to corporate logistics, providing B2B transportation plans. For example, the proposal unit uses AI to analyze a company's logistics needs and propose B2B transportation plans. For example, it provides a plan for efficiently transporting large volumes of cargo. The proposal unit also generates optimal transportation plans based on the company's logistics data and proposes them to the company. For example, it proposes efficient transportation routes between multiple bases. The proposal unit also updates B2B transportation plans in real time and provides optimal plans tailored to the company's needs. For example, it proposes plans that can also accommodate sudden transportation requests. This makes it possible to provide transportation plans that also accommodate corporate logistics.

[0064] The suggestion unit can suggest related insurance and additional services based on the plan selected by the user. For example, the suggestion unit uses AI to suggest the optimal insurance plan based on the transportation plan selected by the user. For example, it suggests adding insurance to expensive luggage. The suggestion unit also suggests additional services related to the transportation plan. For example, it provides a packaging service for fragile luggage. The suggestion unit also suggests related services in real time based on the plan selected by the user. For example, it provides a luggage tracking service or a delivery notification service. This makes it possible to provide the user with related insurance and additional services.

[0065] The suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when selecting a transportation plan and continuously suggest optimal plans. For example, the suggestion unit uses the emotion estimation function to analyze the user's facial expressions and voice when selecting a transportation plan and calculate an emotion score. For example, it preferentially suggests plans that evoke strong positive emotions. The suggestion unit also monitors the user's emotional reactions in real time and continuously suggests optimal plans. For example, if the user smiles, it preferentially suggests that plan. The suggestion unit also learns plans that evoke positive emotions in the user based on the emotion estimation data and provides optimal plans. For example, it proposes plans that reflect the user's preferred means of transportation and routes. This makes it possible to provide optimal transportation plans that match the user's emotions.

[0066] When collecting information on empty trucks on the return journey, the information collection unit can propose eco-friendly transportation plans based on the fuel efficiency or environmental impact of the trucks. For example, the information collection unit uses AI to collect truck fuel efficiency data and propose transportation plans that prioritize the use of fuel-efficient trucks. For example, using fuel-efficient trucks reduces transportation costs. The information collection unit also considers the environmental impact and proposes transportation plans that use low-emission trucks. For example, using environmentally friendly trucks achieves eco-friendly transportation. The information collection unit also integrates truck fuel efficiency data and environmental impact data to propose optimal eco-friendly transportation plans in real time. For example, it provides a plan that takes into account the balance between fuel efficiency and environmental impact. This makes it possible to provide eco-friendly transportation plans.

[0067] When analyzing information on empty trucks on the way home, the information collection unit can propose an optimal schedule based on the truck driver's break times or working conditions. For example, the information collection unit uses AI to collect data on truck drivers' break times and propose an optimal schedule that takes break times into consideration. For example, it may provide a plan that includes appropriate breaks to reduce driver fatigue. The information collection unit may also consider working conditions and propose a schedule that complies with the driver's working hours. For example, it may provide a plan that ensures operation within statutory working hours. The information collection unit may also integrate the truck driver's break times and working conditions and propose an optimal schedule in real time. For example, it may provide a plan that takes into consideration the driver's health and safety. This makes it possible to provide an optimal schedule that takes into consideration the truck driver's break times and working conditions.

[0068] The information collection unit can use the emotion estimation function to analyze the emotions of the user when using an available truck for the return trip and propose a plan that elicits positive emotions. For example, the information collection unit uses the emotion estimation function to analyze the user's facial expressions and voice when using an available truck for the return trip and calculate an emotion score. For example, it preferentially proposes plans that evoke strong positive emotions. The information collection unit also monitors the user's emotional reactions in real time and optimizes plans that elicit positive emotions. For example, if the user smiles, it preferentially proposes that plan. The information collection unit also learns plans that evoke positive emotions in the user based on the emotion estimation data and provides an optimal plan. For example, it proposes a plan that reflects the user's preferred means of transportation and route. This makes it possible to provide a transportation plan that elicits positive emotions in the user.

[0069] The information gathering unit can expand the scope of use of empty trucks on the return journey to include not only domestic but also international transport, thereby providing global transport plans. For example, the information gathering unit uses AI to collect information on empty trucks for international transport and propose global transport plans. For example, it can utilize international logistics networks to provide optimal transport routes. The information gathering unit also integrates data from domestic and international truck transport companies to propose plans that also support international transport. For example, it can provide intermodal transport plans from domestic to overseas. The information gathering unit also updates information on empty trucks for international transport in real time, providing users with the latest global transport plans. For example, it can provide plans that reflect international logistics trends. This makes it possible to provide global transport plans.

[0070] When collecting information on empty trucks on the way back, the information collection unit can track the truck's location information in real time and notify the user of the situation in real time. For example, the information collection unit uses AI to track the truck's location information in real time and notify the user of its current location and estimated arrival time. For example, it uses GPS data to display the truck's movement in real time. The information collection unit also builds a system that notifies the user of the situation in real time based on the truck's location information. For example, it sends a notification when the truck approaches its destination. The information collection unit also updates the truck's location information in real time to provide the user with the latest status. For example, it immediately notifies the user if the truck is delayed or its route is changed. This allows the truck's location information to be tracked in real time and the user to be provided with the latest status.

[0071] The information collection unit can use the emotion estimation function to analyze the truck driver's emotions and propose a schedule to reduce the driver's stress. For example, the information collection unit uses the emotion estimation function to analyze the truck driver's facial expressions and voice and calculate an emotion score. For example, if stress is high, it can suggest taking a break. The information collection unit also monitors the driver's emotional reactions in real time and optimizes a schedule to reduce stress. For example, if the driver is tired, it can propose a plan that includes a break. The information collection unit also learns schedules to reduce driver stress based on the emotion estimation data and proposes an optimal schedule. For example, it can propose routes and rest points where the driver can relax. This makes it possible to provide a schedule that reduces the truck driver's stress.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The information collection unit can also collect the user's health data and propose an optimal transportation plan based on the user's health condition. For example, if the user cannot tolerate long travel times, it can prioritize a route that will arrive in the shortest time. The information collection unit can also collect the user's allergy information and propose transportation methods that take allergies into consideration. For example, for a user who is allergic to pets, it can propose a bus that does not allow pets. The information collection unit can also propose a plan to provide a comfortable travel environment based on the user's health data. For example, for a user who suffers from back pain, it can propose a bus with reclining seats. This makes it possible to provide transportation plans that take the user's health condition into consideration.

[0074] The analysis unit can also provide tourist information and event information related to the transportation plan based on the user's hobbies and interests. For example, if the user is interested in music festivals, it can provide information about music events held in that area. The analysis unit can also suggest information about tourist spots and restaurants based on the user's hobbies. For example, if a user is a food lover, it can suggest popular restaurants near the destination. The analysis unit can also provide special offers and discount information related to the transportation plan based on the user's interests. For example, it can provide discount tickets to museums. This makes it possible to provide transportation plans with added value that match the user's hobbies and interests.

[0075] The suggestion unit can also take into account the user's social connections and suggest transportation plans that can be used with friends and family. For example, it can integrate availability information for friends and family heading to the same destination and suggest plans that allow them to travel together. The suggestion unit can also analyze the user's social media data and refer to transportation plans used by friends and family. For example, it can prioritize and suggest plans that have been highly rated by friends. The suggestion unit can also offer group discounts and special offers based on the user's social connections. For example, it can apply discounts when multiple people use the service. This makes it possible to provide transportation plans that take into account the user's social connections.

[0076] The information collection unit can use the emotion estimation function to analyze the emotions of a user when viewing availability information and optimize an information presentation method that elicits positive emotions. For example, the emotion estimation function can be used to analyze facial expressions and voices of a user when viewing availability information and calculate an emotion score. For example, an information presentation method that elicits strong positive emotions can be preferentially adopted. The information collection unit also monitors the user's emotional reactions in real time and optimizes an information presentation method that elicits positive emotions. For example, if the user smiles, that information presentation method can be continued. The information collection unit also learns information presentation methods that evoke positive emotions in the user based on the emotion estimation data and provides the optimal information presentation method. For example, information can be presented that reflects the user's preferred colors and designs. This makes it possible to provide an information presentation method that elicits positive emotions in the user.

[0077] The information collection unit can expand the scope of vacancy information collection to include not only express buses or transport trucks but also taxis or shared ride services, thereby providing comprehensive vacancy information for transportation means. For example, AI can collect vacancy information for taxis and shared ride services and provide comprehensive vacancy information for transportation means. For example, it can display taxi vacancies and available times for shared rides. The information collection unit can also integrate vacancy information for express buses and transport trucks with vacancy information for taxis and shared ride services to suggest the optimal transportation means to users. For example, it can suggest taxis or shared rides when a bus is fully booked. The information collection unit can also update vacancy information for taxis and shared ride services in real time to provide users with the latest information. For example, it can instantly reflect taxi location information and shared ride reservation status. This makes it possible to provide comprehensive vacancy information for transportation means.

[0078] The information collection unit can also incorporate image or video data, allowing users to visually check availability. For example, AI can collect images and videos of the interior of buses and trucks, allowing users to visually check availability. For example, images can be displayed showing seat availability and trunk space. The information collection unit can also use image recognition technology to automatically analyze availability from the collected image and video data and provide the results to users. For example, image analysis can be used to determine seat availability and display availability information. The information collection unit can also update image and video data in real time, allowing users to visually check availability. For example, live video of the interior of buses and trucks can be provided, allowing users to instantly check availability. This allows users to visually check availability.

[0079] The information collection unit can use the emotion estimation function to estimate the emotion of the user when entering availability information in real time, and provide an interface for reducing stress. For example, the emotion estimation function can be used to analyze the facial expression and voice of the user when entering availability information, and calculate an emotion score. For example, if the user is feeling stressed, the interface can be simplified. The information collection unit can also monitor the user's emotional reactions in real time, and provide an interface for reducing stress. For example, if the user is confused, a help message can be displayed. The information collection unit can also learn an interface that is less stressful for the user based on the emotion estimation data, and provide an optimal interface. For example, an interface that reflects the user's preferred colors and designs can be provided. This makes it possible to provide an interface that reduces user stress.

[0080] The proposal unit can generate transportation plans that provide higher satisfaction by reflecting the user's past ratings and feedback. For example, AI can analyze the user's past ratings and feedback to generate transportation plans that provide higher satisfaction. For example, it can prioritize proposals of plans that have received high ratings in the past. The proposal unit can also identify areas for improvement in the transportation plan based on the user's feedback and reflect these in the next proposal. For example, it can propose plans that improve on areas that users were dissatisfied with in the past. The proposal unit can also collect user rating data in real time and immediately reflect this data to always provide the latest plans that provide higher satisfaction. For example, if the user's rating is low, it will immediately regenerate a plan. This makes it possible to provide transportation plans that increase user satisfaction.

[0081] The suggestion unit can select the optimal means of transport based on the type or characteristics of the cargo. For example, AI analyzes the type and characteristics of the cargo and selects the optimal means of transport. For example, for fragile cargo, it may suggest a truck equipped with shock-absorbing materials. The suggestion unit may also suggest a refrigerated truck if refrigeration is required, depending on the characteristics of the cargo. For example, it may preferentially select a refrigerated truck for transporting fresh food or medicine. The suggestion unit may also select the optimal means of transport in real time based on the type and characteristics of the cargo and suggest it to the user. For example, it may select an appropriate truck depending on the weight and size of the cargo. This makes it possible to provide the optimal means of transport based on the type and characteristics of the cargo.

[0082] The suggestion unit can use the emotion estimation function to analyze the user's emotions regarding the proposed plan and preferentially suggest plans that elicit positive emotions. For example, the emotion estimation function can be used to analyze the user's facial expressions and voice regarding the proposed plan and calculate an emotion score. For example, plans that evoke strong positive emotions can be preferentially suggested. The suggestion unit can also monitor the user's emotional reactions in real time and optimize plans that elicit positive emotions. For example, if the user smiles, that plan can be preferentially suggested. The suggestion unit can also learn plans that evoke positive emotions in the user based on the emotion estimation data and provide optimal plans. For example, it can propose plans that reflect the user's preferred means of transportation and routes. This makes it possible to provide transportation plans that elicit positive emotions in the user.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The information collection unit collects information on vacant express buses or transport trucks. For example, the information collection unit collects data such as the operation schedule, current number of passengers, and loading status of each bus or truck. The information collection unit can also collect operation information in real time and analyze information on vacant seats and trunk space. For example, the information collection unit uses GPS data to collect location information of buses and trucks and determine vacant status. Step 2: The analysis unit analyzes the availability information collected by the information collection unit. For example, the analysis unit analyzes the status of available seats and trunk space based on the collected data. The analysis unit can also use AI to analyze the data and generate an optimal transportation plan for the user. For example, the analysis unit uses a machine learning algorithm to predict availability based on past data. Step 3: The proposal unit proposes an optimal transportation plan based on the information analyzed by the analysis unit. For example, the proposal unit generates an optimal transportation plan based on the user's departure point, destination, and desired transportation date and time. The proposal unit can also use a generation AI to propose an optimal plan to the user. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an optimal plan for the user. Step 4: The notification unit notifies the user of the transportation plan proposed by the proposal unit. For example, the notification unit sends the user an email, an SMS, or an app notification. The notification unit can also automatically confirm the reservation and send a confirmation notification when the user agrees to the proposed plan. For example, the notification unit sends a reservation confirmation email after the user agrees to the plan.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 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 information gathering unit that gathers information on vacant express buses or transport trucks; an analysis unit that analyzes the availability information collected by the information collection unit; a proposal unit that proposes an optimal transportation plan based on the information analyzed by the analysis unit; a notification unit that notifies a user of the transportation plan proposed by the proposal unit. A system characterized by:

2. The information collecting unit Providing more accurate availability information based on at least one external data point, such as weather or traffic conditions 2. The system of claim 1.

3. The information collecting unit Expand the scope of vacancy information collection to include not only express buses and transport trucks but also taxis and shared ride services, providing comprehensive vacancy information for transportation modes.

2. The system of claim 1.

4. The proposal unit Generate a transportation plan that will provide greater satisfaction by reflecting the user's past ratings and feedback.

2. The system of claim 1.

5. The information collecting unit Analyze the emotions of the user when using an empty truck on the way back and propose a plan that elicits positive emotions 2. The system of claim 1.

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