system

Abstract

To propose a system that proposes an optimal access method to a destination based on the location information of public transportation.SOLUTION: A system according to an embodiment comprises a collection unit, an analysis unit, a reception unit, and a proposal unit. The collection unit collects location information from GPS devices mounted on public transportation vehicles. The analysis unit analyzes the location information collected by the collection unit to determine the current traffic status. The reception unit receives input of a destination from the user. The proposal unit proposes an access method to the destination based on the analysis results from the analysis 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to quickly respond to delays in public transportation and changes in traffic conditions, and to suggest the most optimal means of access.

[0005] The system according to the embodiment aims to propose the optimal means of access to a destination based on the location information of public transportation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reception unit, and a proposal unit. The collection unit collects location information from GPS devices installed in public transportation vehicles. The analysis unit analyzes the location information collected by the collection unit and grasps the current traffic situation. The reception unit accepts input of a destination from a user. The proposal unit proposes an access means to the destination based on the analysis result by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can suggest the optimal means of access to a destination based on the location information of public transportation. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A traffic guidance system according to an embodiment of the present invention is a system that collects position information from a GPS device installed in a public transportation vehicle, and based on the information, the generation AI accurately advises on the access means to the place where the user wants to go. This traffic guidance system collects position information from a GPS device installed in a public transportation vehicle, and based on the information, the generation AI accurately advises on the access means to the place where the user wants to go. For example, the traffic guidance system collects position information from a GPS device installed in a public transportation vehicle. For example, a GPS device is installed in a vehicle such as a train or bus, and the position information is collected in real time. This information is collected by a collection unit. Next, the traffic guidance system analyzes the collected position information and grasps the current traffic situation. The analysis unit analyzes the current position and operation status of each vehicle based on the collected position information. For example, if a train is delayed, that information is also analyzed. When a user inputs a destination, the reception unit accepts the information. For example, a user inputs "I want to go from station A to station B". This information is accepted by the reception unit. Next, in the traffic guidance system, the generation AI proposes an access means to the destination based on the analysis result. In the suggestion section, the generation AI proposes the optimal means of access based on the analysis results from the analysis section and the information input by the user. For example, if a train is delayed, it can suggest using an alternative route or a bus. This system allows users to know the optimal means of access in real time and can also handle situations such as train delays. For example, if a train is delayed, the generation AI can suggest an alternative route, allowing the user to reach their destination smoothly. This allows the traffic guidance system to provide users with the optimal means of access in real time.

[0029] A traffic guidance system according to an embodiment includes a collection unit, an analysis unit, a reception unit, and a proposal unit. The collection unit collects location information from a GPS device installed in a public transportation vehicle. For example, a GPS device is installed in a vehicle such as a train or bus, and the location information is collected in real time. This information is collected by the collection unit. The collection unit can collect, for example, GPS coordinates and latitude and longitude information. The analysis unit analyzes the collected location information and grasps the current traffic situation. For example, if a train is delayed, the information is also analyzed. The analysis unit can analyze, for example, information such as traffic congestion and operation delays. The reception unit accepts input of a destination from a user. For example, a user inputs "I want to go from station A to station B". This information is accepted by the reception unit. The reception unit can accept, for example, information such as an address or a landmark. The proposal unit proposes an access means to the destination based on the analysis result by the analysis unit. The proposal unit can propose an access means such as a bus, a train, or a taxi. The proposal unit uses a generation AI to propose an optimal access means. For example, the generative AI can suggest the optimal means of access based on the information entered by the user and the analysis results, allowing the traffic guidance system to provide the optimal means of access to the user in real time.

[0030] The collection unit collects location information from GPS devices installed in vehicles of public transportation. For example, GPS devices are installed in vehicles such as trains and buses, and the location information is collected in real time. This information is collected by the collection unit. Specifically, the GPS devices installed in each train vehicle periodically transmit their location information via satellite, and the GPS devices of buses similarly transmit their location information while traveling in real time. The collection unit centrally manages these location information and stores it in a central server. The collected information includes not only GPS coordinates and latitude and longitude information, but also detailed data such as speed, direction of travel, and stop time. This allows the collection unit to grasp the operation status of public transportation in detail. Furthermore, the collection unit updates these data in real time and always keeps the latest information. For example, if a train is delayed or a bus is caught in traffic, that information is also collected immediately and reflected in the entire system. This allows the collection unit to quickly respond to changes in traffic conditions and provide accurate data. The collection unit also has a function of detecting and filtering abnormal and inaccurate data. This ensures the reliability and accuracy of the data, allowing the analysis unit and proposal unit to perform processing based on accurate information.

[0031] The analysis unit analyzes the collected location information and grasps the current traffic situation. For example, if a train is delayed, that information is also analyzed. The analysis unit can analyze information such as traffic congestion and operation delays. Specifically, based on the collected location information, the current position and operation speed of each vehicle are analyzed to grasp the traffic situation in real time. The analysis algorithm using AI compares past operation data and current traffic conditions to detect abnormal delays and congestion. For example, when a delay occurs in a specific section compared with the normal operation schedule, the cause of the delay is identified and the extent of the impact is evaluated. In addition, the analysis unit can integrate data from multiple transportation facilities and grasp the overall traffic situation. For example, the impact of a train delay on bus operation is evaluated and the overall traffic network situation is analyzed. Furthermore, the analysis unit can predict traffic patterns for specific time periods and days of the week based on past data and evaluate the risk of future delays and congestion. As a result, the analysis unit can perform not only real-time traffic conditions but also future risk assessments and predictions, improving the reliability and efficiency of the entire system.

[0032] The reception unit accepts input of a destination from a user. For example, the user inputs "I want to go from station A to station B". This information is accepted by the reception unit. The reception unit can accept information such as addresses and landmarks. Specifically, the reception unit provides an interface for the user to input a destination and a starting point using a smartphone or a personal computer. The user can input a station name, an address, a landmark, or a specific facility name, and the reception unit accurately accepts this information. Furthermore, the reception unit supports various input methods such as voice input and two-dimensional code (e.g., QR code (registered trademark)) scanning, improving user convenience. For example, when a user speaks "I want to go from station A to station B", the content is converted into text using voice recognition technology, and the reception unit accepts it. In addition, the reception unit can learn the user's past input history and preferences, and perform automatic completion and candidate suggestions when inputting. This allows the user to input a destination quickly and easily. Furthermore, the reception unit quickly transmits the input information to the analysis unit and the suggestion unit, enabling real-time processing. This enables the reception unit to process the information input by the user accurately and quickly, thereby improving the efficiency of the entire system.

[0033] The suggestion unit proposes an access means to the destination based on the analysis result by the analysis unit. The suggestion unit can propose an access means such as a bus, a train, or a taxi. The suggestion unit uses the generation AI to propose the optimal access means. For example, the generation AI proposes the optimal access means based on the user's input information and the analysis result. Specifically, the generation AI comprehensively analyzes the departure point and destination entered by the user, the current traffic conditions, the operation schedule, past data, etc., and proposes the optimal route and transportation means. For example, if a train is delayed, the generation AI proposes the use of an alternative bus route or a taxi. In addition, the generation AI can learn the user's past usage history and preferences and make individually customized suggestions. For example, the bus is preferentially suggested to a user who has preferred to use the bus in the past. Furthermore, the suggestion unit can continuously revise the proposal content based on traffic information updated in real time to respond to the latest situation. For example, if new delay information occurs after the proposal, the generation AI immediately calculates a new route and notifies the user. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. This enables the suggestion unit to provide the user with the most suitable access means and assist in comfortable and efficient travel.

[0034] The suggestion unit can re-suggest an access means to the destination based on the analysis result by the analysis unit. The suggestion unit re-suggests the optimal access means according to, for example, changes in traffic conditions. For example, if a train is delayed, it can re-suggest using a different route or a bus. The suggestion unit makes the re-suggestion using a generation AI. For example, the generation AI analyzes changes in traffic conditions in real time and re-suggests the optimal access means. This allows the traffic guidance system to provide the user with the optimal access means according to changes in traffic conditions.

[0035] The collection unit can apply different collection algorithms depending on the type of public transportation. For example, in the case of a train, the collection unit adjusts the collection frequency depending on the distance between stations. In addition, in the case of a bus, the collection unit can adjust the collection frequency depending on the distance between stops. Furthermore, in the case of a tram, the collection unit can adjust the collection frequency depending on the congestion status of the line. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection frequency using an AI model that applies different collection algorithms depending on the type of public transportation. This enables the collection unit to collect optimal location information according to the type of public transportation.

[0036] The collection unit can adjust the method of collecting location information based on weather information. For example, in rainy weather, the collection unit can increase the frequency of collecting location information to quickly grasp delay information. In addition, in fine weather, the collection unit can reduce the frequency of collecting location information to reduce battery consumption. Furthermore, on snowy days, the collection unit can increase the frequency of collecting location information to provide information taking into account road surface conditions. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection frequency using an AI model that adjusts the method of collecting location information based on weather information. This enables the collection unit to collect appropriate location information according to the weather.

[0037] The collection unit can adjust the collection timing in consideration of the operation schedule of the public transportation. The collection unit collects the position information before arrival at the station based on, for example, a train operation schedule. The collection unit can also collect the position information before arrival at the bus stop based on a bus operation schedule. Furthermore, the collection unit can collect the position information before arrival at a major intersection based on a tram operation schedule. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection timing using an AI model that adjusts the collection timing in consideration of the operation schedule of the public transportation. This enables the collection unit to set an appropriate collection timing based on the operation schedule.

[0038] The collection unit can integrate data from multiple GPS devices to improve the accuracy of the location information. The collection unit can, for example, install multiple GPS devices in a railroad car and integrate the data to improve the accuracy. The collection unit can also install multiple GPS devices in a bus car and integrate the data to improve the accuracy. Furthermore, the collection unit can install multiple GPS devices in a tram car and integrate the data to improve the accuracy. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can improve the accuracy of the location information by using an AI model that integrates data from multiple GPS devices. As a result, the collection unit improves the accuracy of the location information by integrating data from multiple GPS devices.

[0039] The analysis unit can predict the current traffic situation by referring to the past traffic data. The analysis unit can predict the current traffic congestion situation based on the past traffic data, for example. The analysis unit can also predict the delay situation of public transportation based on the past traffic data. Furthermore, the analysis unit can predict the traffic situation in a specific time period based on the past traffic data. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can predict the traffic situation using an AI model that predicts the current traffic situation by referring to the past traffic data. This allows the analysis unit to provide more accurate information by predicting the current traffic situation based on the past data.

[0040] The analysis unit can improve the analysis accuracy by taking into account traffic accidents and construction information. The analysis unit can propose a detour route based on, for example, traffic accident information. The analysis unit can also propose a detour route based on construction information. The analysis unit can also propose an optimal route based on traffic accidents and construction information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that improves the analysis accuracy by taking into account traffic accidents and construction information. As a result, the analysis unit improves the analysis accuracy by taking into account traffic accidents and construction information.

[0041] The analysis unit can adjust the analysis result taking into account the operation schedule of the public transportation. The analysis unit can propose an optimal route based on, for example, a train operation schedule. The analysis unit can also propose an optimal route based on a bus operation schedule. The analysis unit can also propose an optimal route based on a tram operation schedule. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can adjust the analysis result using an AI model that adjusts the analysis result taking into account the operation schedule of the public transportation. As a result, the analysis unit can provide an appropriate analysis result based on the operation schedule.

[0042] The analysis unit can improve the analysis accuracy by integrating information on other means of transportation. The analysis unit can, for example, integrate the position information of taxis to propose an optimal route. The analysis unit can also integrate the position information of bicycles to propose an optimal route. The analysis unit can also integrate the position information of other means of transportation to propose an optimal route. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that integrates information on other means of transportation to improve the analysis accuracy. As a result, the analysis unit improves the analysis accuracy by integrating information on other means of transportation.

[0043] The reception unit can provide input assistance by referring to the user's past destination history. For example, the reception unit automatically displays destinations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations to be used in a specific time period from the user's past input history. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can provide input assistance by using an AI model that provides input assistance by referring to the user's past destination history. This enables the reception unit to provide input assistance based on the past history.

[0044] The reception unit can support various input methods such as voice input and gesture input. For example, when the user uses voice input, the reception unit inputs the destination using voice recognition technology. Furthermore, when the user uses gesture input, the reception unit can input the destination using gesture recognition technology. Furthermore, when the user uses text input, the reception unit can support keyboard input. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can support the input method using an AI model that supports various input methods such as voice input and gesture input. As a result, the reception unit supports various input methods, thereby improving the convenience of the user.

[0045] The reception unit can automatically acquire the user's current location information to simplify the destination input. For example, when the user opens the app, the reception unit automatically acquires the current location and sets it as the starting location. In addition, when the user inputs the destination, the reception unit can suggest the optimal candidate location in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the starting location. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can acquire the current location information using an AI model that automatically acquires the user's current location information to simplify the destination input. As a result, the reception unit automatically acquires the current location information, thereby simplifying the destination input.

[0046] The reception unit can propose an optimal destination in consideration of the user's schedule information. The reception unit, for example, automatically proposes the next planned location based on the user's schedule. The reception unit can also propose a location related to a specific event as a candidate location from the user's schedule information. Furthermore, the reception unit can propose an optimal route according to the schedule based on the user's schedule information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can propose a destination using an AI model that proposes an optimal destination in consideration of the user's schedule information. This enables the reception unit to propose an optimal destination based on the schedule information.

[0047] The suggestion unit can update the proposal content in real time according to changes in traffic conditions. For example, the suggestion unit can suggest a detour route when traffic congestion occurs. Furthermore, the suggestion unit can suggest another means of transportation when a delay occurs in public transportation. Furthermore, the suggestion unit can suggest an optimal detour route when a traffic accident occurs. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can update the proposal content using a generation AI model that updates the proposal content in real time according to changes in traffic conditions. This enables the suggestion unit to make real-time suggestions according to changes in traffic conditions.

[0048] The suggestion unit can make optimal suggestions by referring to the user's past movement history. The suggestion unit makes optimal suggestions based on, for example, routes used by the user in the past. The suggestion unit can also suggest routes that avoid congestion based on the user's past movement history. Furthermore, the suggestion unit can analyze the user's past movement history and suggest the most efficient route. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can make suggestions using a generation AI model that makes optimal suggestions by referring to the user's past movement history. This enables the suggestion unit to make optimal suggestions based on the past movement history.

[0049] The suggestion unit can present multiple options including the use of other means of transportation. For example, the suggestion unit can propose a route including the use of a taxi. The suggestion unit can also propose a route including the use of a bicycle. Furthermore, the suggestion unit can propose an optimal route that combines other means of transportation. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can present the options using a generation AI model that presents multiple options including the use of other means of transportation. In this way, the suggestion unit can provide the user with an optimal means of transportation by presenting multiple options including other means of transportation.

[0050] The suggestion unit can suggest the optimal means of transportation in consideration of the user's schedule information. For example, the suggestion unit can suggest the optimal means of transportation to the next planned location based on the user's schedule. The suggestion unit can also suggest a means of transportation related to a specific event from the user's schedule information. Furthermore, the suggestion unit can suggest an optimal route according to the schedule based on the user's schedule information. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can suggest a means of transportation using a generation AI model that suggests the optimal means of transportation in consideration of the user's schedule information. This enables the suggestion unit to suggest the optimal means of transportation based on the schedule information.

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

[0052] The collection unit can adjust the method of collecting location information based on weather information. For example, in rainy weather, the frequency of collecting location information is increased to quickly grasp delay information. In addition, in sunny weather, the frequency of collecting location information is reduced to reduce battery consumption. Furthermore, on snowy days, the frequency of collecting location information is increased to provide information that takes into account road conditions. This enables the collection unit to collect appropriate location information according to the weather.

[0053] The collection unit can apply different collection algorithms depending on the type of public transportation. For example, in the case of a train, the collection frequency can be adjusted depending on the distance between stations. In addition, in the case of a bus, the collection frequency can be adjusted depending on the distance between stops. Furthermore, in the case of a tram, the collection frequency can be adjusted depending on the congestion status of the route. This enables the collection unit to collect optimal location information depending on the type of public transportation.

[0054] The collection unit can integrate data from multiple GPS devices to improve the accuracy of the location information. For example, multiple GPS devices can be installed in a railroad car and the data can be integrated to improve the accuracy. Also, multiple GPS devices can be installed in a bus car and the data can be integrated to improve the accuracy. Furthermore, multiple GPS devices can be installed in a tram car and the data can be integrated to improve the accuracy. In this way, the collection unit can improve the accuracy of the location information by integrating data from multiple GPS devices.

[0055] The analysis unit can predict the current traffic situation by referring to past traffic data. For example, the current traffic congestion situation can be predicted based on the past traffic data. Also, the delay situation of public transportation can be predicted based on the past traffic data. Furthermore, the traffic situation in a specific time period can be predicted based on the past traffic data. This enables the analysis unit to provide more accurate information by predicting the current traffic situation based on the past data.

[0056] The suggestion unit can present a plurality of options including the use of other means of transportation. For example, the suggestion unit can suggest a route including the use of a taxi. The suggestion unit can also suggest a route including the use of a bicycle. Furthermore, the suggestion unit can suggest an optimal route that combines other means of transportation. In this way, the suggestion unit can provide the user with an optimal means of transportation by presenting a plurality of options including other means of transportation.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The collection unit collects location information from a GPS device installed in a public transportation vehicle. For example, a GPS device is installed in a vehicle such as a train or bus, and the location information is collected in real time. This information is collected by the collection unit. The collection unit can collect, for example, GPS coordinates and latitude and longitude information. Step 2: The analysis unit analyzes the collected location information and understands the current traffic situation. For example, if a train is delayed, that information is also analyzed. The analysis unit can analyze information such as traffic congestion and service delays. Step 3: The reception unit receives input of a destination from the user. For example, the user inputs "I want to go from station A to station B." This information is received by the reception unit. The reception unit can receive information such as addresses and landmarks, for example. Step 4: The suggestion unit suggests an access means to the destination based on the analysis results by the analysis unit. The suggestion unit can suggest access means such as bus, train, taxi, etc. The suggestion unit uses the generation AI to suggest the optimal access means. For example, the generation AI suggests the optimal access means based on the user's input information and the analysis results.

[0059] (Example 2) A traffic guidance system according to an embodiment of the present invention is a system that collects position information from a GPS device installed in a public transportation vehicle, and based on the information, the generation AI accurately advises on the access means to the place where the user wants to go. This traffic guidance system collects position information from a GPS device installed in a public transportation vehicle, and based on the information, the generation AI accurately advises on the access means to the place where the user wants to go. For example, the traffic guidance system collects position information from a GPS device installed in a public transportation vehicle. For example, a GPS device is installed in a vehicle such as a train or bus, and the position information is collected in real time. This information is collected by a collection unit. Next, the traffic guidance system analyzes the collected position information and grasps the current traffic situation. The analysis unit analyzes the current position and operation status of each vehicle based on the collected position information. For example, if a train is delayed, that information is also analyzed. When a user inputs a destination, the reception unit accepts the information. For example, a user inputs "I want to go from station A to station B". This information is accepted by the reception unit. Next, in the traffic guidance system, the generation AI proposes an access means to the destination based on the analysis result. In the suggestion section, the generation AI proposes the optimal means of access based on the analysis results from the analysis section and the information input by the user. For example, if a train is delayed, it can suggest using an alternative route or a bus. This system allows users to know the optimal means of access in real time and can also handle situations such as train delays. For example, if a train is delayed, the generation AI can suggest an alternative route, allowing the user to reach their destination smoothly. This allows the traffic guidance system to provide users with the optimal means of access in real time.

[0060] A traffic guidance system according to an embodiment includes a collection unit, an analysis unit, a reception unit, and a proposal unit. The collection unit collects location information from a GPS device installed in a public transportation vehicle. For example, a GPS device is installed in a vehicle such as a train or bus, and the location information is collected in real time. This information is collected by the collection unit. The collection unit can collect, for example, GPS coordinates and latitude and longitude information. The analysis unit analyzes the collected location information and grasps the current traffic situation. For example, if a train is delayed, the information is also analyzed. The analysis unit can analyze, for example, information such as traffic congestion and operation delays. The reception unit accepts input of a destination from a user. For example, a user inputs "I want to go from station A to station B". This information is accepted by the reception unit. The reception unit can accept, for example, information such as an address or a landmark. The proposal unit proposes an access means to the destination based on the analysis result by the analysis unit. The proposal unit can propose an access means such as a bus, a train, or a taxi. The proposal unit uses a generation AI to propose an optimal access means. For example, the generative AI can suggest the optimal means of access based on the information entered by the user and the analysis results, allowing the traffic guidance system to provide the optimal means of access to the user in real time.

[0061] The collection unit collects location information from GPS devices installed in vehicles of public transportation. For example, GPS devices are installed in vehicles such as trains and buses, and the location information is collected in real time. This information is collected by the collection unit. Specifically, the GPS devices installed in each train vehicle periodically transmit their location information via satellite, and the GPS devices of buses similarly transmit their location information while traveling in real time. The collection unit centrally manages these location information and stores it in a central server. The collected information includes not only GPS coordinates and latitude and longitude information, but also detailed data such as speed, direction of travel, and stop time. This allows the collection unit to grasp the operation status of public transportation in detail. Furthermore, the collection unit updates these data in real time and always keeps the latest information. For example, if a train is delayed or a bus is caught in traffic, that information is also collected immediately and reflected in the entire system. This allows the collection unit to quickly respond to changes in traffic conditions and provide accurate data. The collection unit also has a function of detecting and filtering abnormal and inaccurate data. This ensures the reliability and accuracy of the data, allowing the analysis unit and proposal unit to perform processing based on accurate information.

[0062] The analysis unit analyzes the collected location information and grasps the current traffic situation. For example, if a train is delayed, that information is also analyzed. The analysis unit can analyze information such as traffic congestion and operation delays. Specifically, based on the collected location information, the current position and operation speed of each vehicle are analyzed to grasp the traffic situation in real time. The analysis algorithm using AI compares past operation data and current traffic conditions to detect abnormal delays and congestion. For example, when a delay occurs in a specific section compared with the normal operation schedule, the cause of the delay is identified and the extent of the impact is evaluated. In addition, the analysis unit can integrate data from multiple transportation facilities and grasp the overall traffic situation. For example, the impact of a train delay on bus operation is evaluated and the overall traffic network situation is analyzed. Furthermore, the analysis unit can predict traffic patterns for specific time periods and days of the week based on past data and evaluate the risk of future delays and congestion. As a result, the analysis unit can perform not only real-time traffic conditions but also future risk assessments and predictions, improving the reliability and efficiency of the entire system.

[0063] The reception unit accepts input of a destination from a user. For example, the user inputs "I want to go from station A to station B". This information is accepted by the reception unit. The reception unit can accept information such as addresses and landmarks. Specifically, the reception unit provides an interface for the user to input a destination and a starting point using a smartphone or a personal computer. The user can input a station name, an address, a landmark, or a specific facility name, and the reception unit accurately accepts this information. Furthermore, the reception unit supports various input methods such as voice input and two-dimensional code (e.g., QR code) scanning, improving user convenience. For example, when a user speaks "I want to go from station A to station B", the content is converted into text using voice recognition technology, and the reception unit accepts it. In addition, the reception unit can learn the user's past input history and preferences, and perform automatic completion and candidate suggestions when inputting. This allows the user to input a destination quickly and easily. Furthermore, the reception unit quickly transmits the input information to the analysis unit and the suggestion unit, enabling real-time processing. This allows the reception unit to accurately and quickly process the user's input information, improving the efficiency of the entire system.

[0064] The suggestion unit proposes an access means to the destination based on the analysis result by the analysis unit. The suggestion unit can propose an access means such as a bus, a train, or a taxi. The suggestion unit uses the generation AI to propose the optimal access means. For example, the generation AI proposes the optimal access means based on the user's input information and the analysis result. Specifically, the generation AI comprehensively analyzes the departure point and destination entered by the user, the current traffic conditions, the operation schedule, past data, etc., and proposes the optimal route and transportation means. For example, if a train is delayed, the generation AI proposes the use of an alternative bus route or a taxi. In addition, the generation AI can learn the user's past usage history and preferences and make individually customized suggestions. For example, the bus is preferentially suggested to a user who has preferred to use the bus in the past. Furthermore, the suggestion unit can continuously revise the proposal content based on traffic information updated in real time to respond to the latest situation. For example, if new delay information occurs after the proposal, the generation AI immediately calculates a new route and notifies the user. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. This enables the suggestion unit to provide the user with the most suitable access means and assist in comfortable and efficient travel.

[0065] The suggestion unit can re-suggest an access means to the destination based on the analysis result by the analysis unit. The suggestion unit re-suggests the optimal access means according to, for example, changes in traffic conditions. For example, if a train is delayed, it can re-suggest using a different route or a bus. The suggestion unit makes the re-suggestion using a generation AI. For example, the generation AI analyzes changes in traffic conditions in real time and re-suggests the optimal access means. This allows the traffic guidance system to provide the user with the optimal access means according to changes in traffic conditions.

[0066] The collection unit can estimate the user's emotion and adjust the frequency of collecting location information based on the estimated user's emotion. For example, when the user is stressed, the collection unit can increase the frequency of collecting location information to provide more detailed information. Also, when the user is relaxed, the collection unit can reduce the frequency of collecting location information to reduce battery consumption. Furthermore, when the user is in a hurry, the collection unit can increase the frequency of collecting location information to provide information in real time. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the collection unit to adjust the frequency of collecting location information according to the user's emotion.

[0067] The collection unit can apply different collection algorithms depending on the type of public transportation. For example, in the case of a train, the collection unit adjusts the collection frequency depending on the distance between stations. In addition, in the case of a bus, the collection unit can adjust the collection frequency depending on the distance between stops. Furthermore, in the case of a tram, the collection unit can adjust the collection frequency depending on the congestion status of the line. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection frequency using an AI model that applies different collection algorithms depending on the type of public transportation. This enables the collection unit to collect optimal location information according to the type of public transportation.

[0068] The collection unit can adjust the method of collecting location information based on weather information. For example, in rainy weather, the collection unit can increase the frequency of collecting location information to quickly grasp delay information. In addition, in fine weather, the collection unit can reduce the frequency of collecting location information to reduce battery consumption. Furthermore, on snowy days, the collection unit can increase the frequency of collecting location information to provide information taking into account road surface conditions. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection frequency using an AI model that adjusts the method of collecting location information based on weather information. This enables the collection unit to collect appropriate location information according to the weather.

[0069] The collection unit can estimate the user's emotion and determine the priority of the location information to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit prioritizes collecting location information of major transportation facilities. Also, when the user is relaxed, the collection unit can prioritize collecting location information of surrounding tourist attractions. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting location information related to the shortest route. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the collection unit to collect location information in a priority order according to the user's emotion.

[0070] The collection unit can adjust the collection timing in consideration of the operation schedule of the public transportation. The collection unit collects the position information before arrival at the station based on, for example, a train operation schedule. The collection unit can also collect the position information before arrival at the bus stop based on a bus operation schedule. Furthermore, the collection unit can collect the position information before arrival at a major intersection based on a tram operation schedule. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection timing using an AI model that adjusts the collection timing in consideration of the operation schedule of the public transportation. This enables the collection unit to set an appropriate collection timing based on the operation schedule.

[0071] The collection unit can integrate data from multiple GPS devices to improve the accuracy of the location information. The collection unit can, for example, install multiple GPS devices in a railroad car and integrate the data to improve the accuracy. The collection unit can also install multiple GPS devices in a bus car and integrate the data to improve the accuracy. Furthermore, the collection unit can install multiple GPS devices in a tram car and integrate the data to improve the accuracy. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can improve the accuracy of the location information by using an AI model that integrates data from multiple GPS devices. As a result, the collection unit improves the accuracy of the location information by integrating data from multiple GPS devices.

[0072] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the analysis unit to provide an appropriate display method according to the user's emotion.

[0073] The analysis unit can predict the current traffic situation by referring to the past traffic data. The analysis unit can predict the current traffic congestion situation based on the past traffic data, for example. The analysis unit can also predict the delay situation of public transportation based on the past traffic data. Furthermore, the analysis unit can predict the traffic situation in a specific time period based on the past traffic data. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can predict the traffic situation using an AI model that predicts the current traffic situation by referring to the past traffic data. This allows the analysis unit to provide more accurate information by predicting the current traffic situation based on the past data.

[0074] The analysis unit can improve the analysis accuracy by taking into account traffic accidents and construction information. The analysis unit can propose a detour route based on, for example, traffic accident information. The analysis unit can also propose a detour route based on construction information. The analysis unit can also propose an optimal route based on traffic accidents and construction information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that improves the analysis accuracy by taking into account traffic accidents and construction information. As a result, the analysis unit improves the analysis accuracy by taking into account traffic accidents and construction information.

[0075] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, when the user is stressed, the analysis unit can preferentially display information on major transportation facilities. Furthermore, when the user is relaxed, the analysis unit can preferentially display information on nearby tourist spots. Furthermore, when the user is in a hurry, the analysis unit can preferentially display information related to the shortest route. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the analysis unit to provide analysis results in a priority order according to the user's emotion.

[0076] The analysis unit can adjust the analysis result taking into account the operation schedule of the public transportation. The analysis unit can propose an optimal route based on, for example, a train operation schedule. The analysis unit can also propose an optimal route based on a bus operation schedule. The analysis unit can also propose an optimal route based on a tram operation schedule. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can adjust the analysis result using an AI model that adjusts the analysis result taking into account the operation schedule of the public transportation. As a result, the analysis unit can provide an appropriate analysis result based on the operation schedule.

[0077] The analysis unit can improve the analysis accuracy by integrating information on other means of transportation. The analysis unit can, for example, integrate the position information of taxis to propose an optimal route. The analysis unit can also integrate the position information of bicycles to propose an optimal route. The analysis unit can also integrate the position information of other means of transportation to propose an optimal route. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can improve the analysis accuracy by using an AI model that integrates information on other means of transportation to improve the analysis accuracy. As a result, the analysis unit improves the analysis accuracy by integrating information on other means of transportation.

[0078] The reception unit can estimate the user's emotion and adjust the destination input interface based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the destination. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the reception unit to provide an appropriate interface according to the user's emotion.

[0079] The reception unit can provide input assistance by referring to the user's past destination history. For example, the reception unit automatically displays destinations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations to be used in a specific time period from the user's past input history. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can provide input assistance by using an AI model that provides input assistance by referring to the user's past destination history. This enables the reception unit to provide input assistance based on the past history.

[0080] The reception unit can support various input methods such as voice input and gesture input. For example, when the user uses voice input, the reception unit inputs the destination using voice recognition technology. Furthermore, when the user uses gesture input, the reception unit can input the destination using gesture recognition technology. Furthermore, when the user uses text input, the reception unit can support keyboard input. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can support the input method using an AI model that supports various input methods such as voice input and gesture input. As a result, the reception unit supports various input methods, thereby improving the convenience of the user.

[0081] The reception unit can estimate the user's emotion and determine the priority of destination input based on the estimated user's emotion. For example, when the user is stressed, the reception unit can preferentially display major destinations. Furthermore, when the user is relaxed, the reception unit can preferentially display surrounding tourist spots. Furthermore, when the user is in a hurry, the reception unit can preferentially display destinations related to the shortest route. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This enables the reception unit to input destinations in a priority order according to the user's emotion.

[0082] The reception unit can automatically acquire the user's current location information to simplify the destination input. For example, when the user opens the app, the reception unit automatically acquires the current location and sets it as the starting location. In addition, when the user inputs the destination, the reception unit can suggest the optimal candidate location in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the starting location. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can acquire the current location information using an AI model that automatically acquires the user's current location information to simplify the destination input. As a result, the reception unit automatically acquires the current location information, thereby simplifying the destination input.

[0083] The reception unit can propose an optimal destination in consideration of the user's schedule information. The reception unit, for example, automatically proposes the next planned location based on the user's schedule. The reception unit can also propose a location related to a specific event as a candidate location from the user's schedule information. Furthermore, the reception unit can propose an optimal route according to the schedule based on the user's schedule information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can propose a destination using an AI model that proposes an optimal destination in consideration of the user's schedule information. This enables the reception unit to propose an optimal destination based on the schedule information.

[0084] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated user's emotion. For example, when the user is nervous, the suggestion unit makes a simple and highly visible suggestion. Furthermore, when the user is relaxed, the suggestion unit can make a suggestion including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can make a suggestion that focuses on the main points. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the suggestion unit provides an appropriate suggestion according to the user's emotion.

[0085] The suggestion unit can update the proposal content in real time according to changes in traffic conditions. For example, the suggestion unit can suggest a detour route when traffic congestion occurs. Furthermore, the suggestion unit can suggest another means of transportation when a delay occurs in public transportation. Furthermore, the suggestion unit can suggest an optimal detour route when a traffic accident occurs. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can update the proposal content using a generation AI model that updates the proposal content in real time according to changes in traffic conditions. This enables the suggestion unit to make real-time suggestions according to changes in traffic conditions.

[0086] The suggestion unit can make optimal suggestions by referring to the user's past movement history. The suggestion unit makes optimal suggestions based on, for example, routes used by the user in the past. The suggestion unit can also suggest routes that avoid congestion based on the user's past movement history. Furthermore, the suggestion unit can analyze the user's past movement history and suggest the most efficient route. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can make suggestions using a generation AI model that makes optimal suggestions by referring to the user's past movement history. This enables the suggestion unit to make optimal suggestions based on the past movement history.

[0087] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, when the user is feeling stressed, the suggestion unit can preferentially suggest information on major transportation facilities. Furthermore, when the user is relaxed, the suggestion unit can preferentially suggest information on nearby tourist spots. Furthermore, when the user is in a hurry, the suggestion unit can preferentially suggest information related to the shortest route. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables the suggestion unit to make suggestions in order of priority according to the user's emotion.

[0088] The suggestion unit can present multiple options including the use of other means of transportation. For example, the suggestion unit can propose a route including the use of a taxi. The suggestion unit can also propose a route including the use of a bicycle. Furthermore, the suggestion unit can propose an optimal route that combines other means of transportation. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can present the options using a generation AI model that presents multiple options including the use of other means of transportation. In this way, the suggestion unit can provide the user with an optimal means of transportation by presenting multiple options including other means of transportation.

[0089] The suggestion unit can suggest the optimal means of transportation in consideration of the user's schedule information. For example, the suggestion unit can suggest the optimal means of transportation to the next planned location based on the user's schedule. The suggestion unit can also suggest a means of transportation related to a specific event from the user's schedule information. Furthermore, the suggestion unit can suggest an optimal route according to the schedule based on the user's schedule information. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can suggest a means of transportation using a generation AI model that suggests the optimal means of transportation in consideration of the user's schedule information. This enables the suggestion unit to suggest the optimal means of transportation based on the schedule information.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible suggestion can be made. If the user is relaxed, a suggestion including detailed information can be made. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be made. The estimation of emotions is realized using an emotion engine or a generation AI. As a result, the suggestion unit can provide appropriate suggestions according to the user's emotions.

[0092] The collection unit can estimate the user's emotions and adjust the frequency of location information collection based on the estimated user's emotions. For example, when the user is stressed, the frequency of location information collection can be increased to provide more detailed information. When the user is relaxed, the frequency of location information collection can be reduced to reduce battery consumption. Furthermore, when the user is in a hurry, the frequency of location information collection can be increased to provide information in real time. The estimation of emotions is realized using an emotion engine or a generation AI. This allows the collection unit to adjust the frequency of location information collection according to the user's emotions.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. The estimation of emotions is achieved using an emotion engine or a generation AI. This allows the analysis unit to provide an appropriate display method according to the user's emotions.

[0094] The reception unit can estimate the user's emotion and adjust the destination input interface based on the estimated user's emotion. For example, if the user is stressed, a simple interface can be provided to minimize the input procedure. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the destination to be input quickly. The emotion estimation is realized using an emotion engine or a generation AI. This allows the reception unit to provide an appropriate interface according to the user's emotion.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, information on major transportation facilities can be preferentially suggested. If the user is relaxed, information on nearby tourist spots can be preferentially suggested. Furthermore, if the user is in a hurry, information related to the shortest route can be preferentially suggested. The estimation of emotions is realized using an emotion engine or a generation AI. This enables the suggestion unit to make suggestions in order of priority according to the user's emotions.

[0096] The collection unit can adjust the method of collecting location information based on weather information. For example, in rainy weather, the frequency of collecting location information is increased to quickly grasp delay information. In addition, in sunny weather, the frequency of collecting location information is reduced to reduce battery consumption. Furthermore, on snowy days, the frequency of collecting location information is increased to provide information that takes into account road conditions. This enables the collection unit to collect appropriate location information according to the weather.

[0097] The collection unit can apply different collection algorithms depending on the type of public transportation. For example, in the case of a train, the collection frequency can be adjusted depending on the distance between stations. In addition, in the case of a bus, the collection frequency can be adjusted depending on the distance between stops. Furthermore, in the case of a tram, the collection frequency can be adjusted depending on the congestion status of the route. This enables the collection unit to collect optimal location information depending on the type of public transportation.

[0098] The collection unit can integrate data from multiple GPS devices to improve the accuracy of the location information. For example, multiple GPS devices can be installed in a railroad car and the data can be integrated to improve the accuracy. Also, multiple GPS devices can be installed in a bus car and the data can be integrated to improve the accuracy. Furthermore, multiple GPS devices can be installed in a tram car and the data can be integrated to improve the accuracy. In this way, the collection unit can improve the accuracy of the location information by integrating data from multiple GPS devices.

[0099] The analysis unit can predict the current traffic situation by referring to past traffic data. For example, the current traffic congestion situation can be predicted based on the past traffic data. Also, the delay situation of public transportation can be predicted based on the past traffic data. Furthermore, the traffic situation in a specific time period can be predicted based on the past traffic data. This enables the analysis unit to provide more accurate information by predicting the current traffic situation based on the past data.

[0100] The suggestion unit can present a plurality of options including the use of other means of transportation. For example, the suggestion unit can suggest a route including the use of a taxi. The suggestion unit can also suggest a route including the use of a bicycle. Furthermore, the suggestion unit can suggest an optimal route that combines other means of transportation. In this way, the suggestion unit can provide the user with an optimal means of transportation by presenting a plurality of options including other means of transportation.

[0101] The process flow of the second embodiment will be briefly described below.

[0102] Step 1: The collection unit collects location information from a GPS device installed in a public transportation vehicle. For example, a GPS device is installed in a vehicle such as a train or bus, and the location information is collected in real time. This information is collected by the collection unit. The collection unit can collect, for example, GPS coordinates and latitude and longitude information. Step 2: The analysis unit analyzes the collected location information and understands the current traffic situation. For example, if a train is delayed, that information is also analyzed. The analysis unit can analyze information such as traffic congestion and service delays. Step 3: The reception unit receives input of a destination from the user. For example, the user inputs "I want to go from station A to station B." This information is received by the reception unit. The reception unit can receive information such as addresses and landmarks, for example. Step 4: The suggestion unit suggests an access means to the destination based on the analysis results by the analysis unit. The suggestion unit can suggest access means such as bus, train, taxi, etc. The suggestion unit uses the generation AI to suggest the optimal access means. For example, the generation AI suggests the optimal access means based on the user's input information and the analysis results.

[0103] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0105] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0106] For example, the collection unit can collect location information using the camera 42 or GPS device of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected location information to grasp the current traffic situation. The reception unit receives input from the user using the touch panel 38A or microphone 38B of the smart device 14. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests the optimal access means using a generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0110] 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.

[0111] 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 the voice according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0113] 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 performed in a secure state.

[0114] Fig. 4 shows an example of 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.

[0115] 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.

[0116] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0120] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0121] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] For example, the collection unit can collect location information using the camera 42 or GPS device of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected location information to grasp the current traffic situation. The reception unit receives input from the user using the microphone 238 of the smart glasses 214. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests the optimal access means using a generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0126] 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.

[0127] 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 the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] 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 performed in a secure state.

[0130] 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.

[0131] 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.

[0132] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 voice indicating a user input for 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.

[0136] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] For example, the collection unit can collect location information using the camera 42 or GPS device of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected location information to grasp the current traffic situation. The reception unit receives input from the user using the microphone 238 of the headset type terminal 314. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests the optimal access means using a generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0142] 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.

[0143] 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 the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0145] 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 performed in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0147] 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.

[0148] 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.

[0149] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0151] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] 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 a voice indicating a user input for 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.

[0153] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0155] For example, the collection unit can collect location information using the camera 42 and GPS device of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected location information to grasp the current traffic situation. The reception unit receives input from the user using the microphone 238 of the robot 414. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests the optimal access means using a generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0157] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0159] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0161] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0163] 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.

[0164] 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 Universal Serial Bus (USB) 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.

[0165] In addition, 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 upon request from the data processing device 12.

[0166] 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.

[0167] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0168] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0169] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0170] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0172] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0174] (Appendix 1) A collection unit that collects location information from GPS devices installed in vehicles of the public transportation system; an analysis unit that analyzes the location information collected by the collection unit and grasps a current traffic situation; A reception unit that receives an input of a destination from a user; a suggestion unit that suggests an access means to the destination based on the analysis result by the analysis unit; A system characterized by: (Appendix 2) The suggestion unit, Based on the analysis result by the analysis unit, a new access means to the destination is proposed. 2. The system of claim 1, (Appendix 3) The collecting unit includes: A user's emotion is estimated, and the frequency of location information collection is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 4) The collecting unit includes: Apply different collection algorithms depending on the type of public transport 2. The system of claim 1, (Appendix 5) The system described in Appendix 1, characterized in that the collection unit adjusts a method of collecting location information based on weather information. (Appendix 6) The collecting unit includes: The user's emotions are estimated, and the priority of location information to be collected is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Adjust collection times to take into account public transport schedules 2. The system of claim 1, (Appendix 8) The collecting unit includes: Combining data from multiple GPS devices to improve location accuracy 2. The system of claim 1, (Appendix 9) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 10) The analysis unit is Predicting current traffic conditions by referencing past traffic data 2. The system of claim 1, (Appendix 11) The analysis unit is Improve analysis accuracy by taking into account traffic accident and construction information 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 13) The analysis unit is Adjust your analysis results to account for public transit schedules 2. The system of claim 1, (Appendix 14) The analysis unit is Improve analysis accuracy by integrating information from other modes of transportation 2. The system of claim 1, (Appendix 15) The reception unit is To estimate a user's emotion and adjust a destination input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The reception unit is Provides input assistance by referencing the user's past destination history 2. The system of claim 1, (Appendix 17) The reception unit is Supports various input methods such as voice input and gesture input 2. The system of claim 1, (Appendix 18) The reception unit is Estimate user's emotions and prioritize destination inputs based on the estimated user's emotions 2. The system of claim 1, (Appendix 19) The reception unit is Simplify destination input by automatically acquiring the user's current location information 2. The system of claim 1, (Appendix 20) The reception unit is Suggesting optimal destinations based on user schedule information 2. The system of claim 1, (Appendix 21) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The suggestion unit, Update suggestions in real time as traffic conditions change 2. The system of claim 1, (Appendix 23) The suggestion unit, Make optimal suggestions by referencing the user's past movement history 2. The system of claim 1, (Appendix 24) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 25) The suggestion unit, Present multiple options, including the use of alternative means of transportation 2. The system of claim 1, (Appendix 26) The suggestion unit, Suggesting optimal transportation methods based on user schedule information 2. The system of claim 1, [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects location information from GPS devices installed in vehicles of the public transportation system; an analysis unit that analyzes the location information collected by the collection unit and grasps a current traffic situation; A reception unit that receives an input of a destination from a user; a suggestion unit that suggests an access means to the destination based on the analysis result by the analysis unit; A system characterized by:

2. The suggestion unit, Based on the analysis result by the analysis unit, a new access means to the destination is proposed.

2. The system of claim 1 .

3. The collecting unit includes: A user's emotions are estimated, and the frequency of location information collection is adjusted based on the estimated user's emotions.

2. The system of claim 1 .

4. The collecting unit includes: Apply different collection algorithms depending on the type of public transport 2. The system of claim 1 .

5. The system according to claim 1 , wherein the collection unit adjusts a method of collecting location information based on weather information.

6. The collecting unit includes: The user's emotions are estimated, and the priority of location information to be collected is determined based on the estimated user's emotions.

2. The system of claim 1 .

7. The collecting unit includes: Adjust collection times to take into account public transport schedules 2. The system of claim 1 .

8. The collecting unit includes: Combining data from multiple GPS devices to improve location accuracy 2. The system of claim 1 .

9. The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1 .

10. The analysis unit is Predicting current traffic conditions by referencing past traffic data 2. The system of claim 1 .

Images